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The disposal of biological waste into water bodies is a major global concern as it leads to water pollution resulting in the loss of plenty of revenue in the cleaning of water bodies. Here, in the present research work, sacred flowers were collected, segregated, sun-dried, and powdered. The dried floral powders (marigold and rose) were characterized by field emission scanning electron microscopy (FESEM), electron diffraction spectroscopy (EDS), Fourier transforms infrared spectroscopy (FT-IR), and X-ray diffraction (XRD). The microscopy revealed the irregular spherical shape of the sheet-like structure whose size varies in microns. The EDS revealed the elemental composition which was dominated by mainly carbon and oxygen. The XRD shows the presence of carbon (10-25 ɵ) in the amorphous form and the absence of any crystalline phase in the biosorbents. The FT-IR showed peaks that conformed to the presence of functional groups like -OH and a carbonyl group. The dried powders were used as an economical and eco-friendly biosorbent for the removal of methyl red (MR) dye from the aqueous solutions by batch adsorption study. After 60 minutes of contact time, the marigold powder (MGP) and rose petal powder (RPP) showed decolorization of 61.16% and 56.08% for 2 ppm of MR dye. The kinetic revealed that the dye removal reaction does not follow the pseudo-first-order as well as the pseudo-second-order. The utilization of such waste-based biosorbents will minimize solid waste and also will provide an economical biosorbent for the removal of environmental pollutants. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate the quality of groundwater used for irrigating agricultural areas in the region of Ouargla by using several quality indices and to map the spatial distribution of these indices. This spatial mapping will help create a model for the classification of these resources according to their suitability for irrigation. For this purpose, physicochemical analyses were carried out on samples from 38 wells distributed over the entire territory of the studied region. These include 27 wells that capture Mio-Pliocene groundwater (Terminal Complex) and 11 wells in the Albian groundwater (Intercalary Continental). The thematic maps were developed using a geographic information system (GIS). In this study, we used the following eight indices: potential salinity (PS); residual sodium carbonate (RSC); electrical conductivity (EC); magnesium percent (%Mg2+); sodium percent (%Na+); permeability index (PI); Kelley's ratio (KR); sodium adsorption ratio (SAR). The global qualitative study of the water used for irrigation shows that these resources fall into three categories (good, permissible, and poor for irrigation). Water quality analysis shows that, based on the magnesium percent (%Mg2+), 18% of the wells can be considered to be of good quality, 74% of the wells are of medium quality (permissible for irrigation), and 8% are of poor quality (unsuitable for irrigation). According to the Wilcox diagram, the waters used in the Ouargla region are of very poor quality. Very excessive mineralization, expressed by the electrical conductivity, was observed for the Mio-Pliocene and Albian waters, where it varies with values in the range of 2340 µS/m to 6520 µS/m and 2330 µS/m to 3840 µS/m, respectively. This conductivity presents a high risk of alkalinization. [ABSTRACT FROM AUTHOR]

Lakes help increase the sustainability of the natural environment and decrease food chain risk, agriculture, ecosystem services, and leisure recreational activities locally and globally. Reliable simulation of monthly lake water levels is still an ongoing demand for multiple environmental and hydro-informatics engineering applications. The current research aims to utilize newly developed hybrid data-intelligence models based on the ensemble adaptive neuro-fuzzy inference system (ANFIS) coupled with metaheuristics algorithms for lake water-level simulation by considering the effect of seasonality on Titicaca Lake water-level fluctuations. The classical ANFIS model was trained using three metaheuristics nature-inspired optimization algorithms, including the genetic algorithm (ANFIS-GA), particle swarm optimizer (ANFIS-PSO), and whale optimization algorithm (ANFIS-WOA). For determining the best set of the input variables, an evolutionary approach based on several lag months has been utilized prior to the lake water-level simulation process using the hybrid models. The proposed hybrid models were investigated for accurately simulating the monthly water levels at Titicaca Lake. The ANFIS-WOA model exhibited the best prediction performance for lake water-level pattern measurement in this study. For the best scenario (the inputs were X t - 1 , X t - 2 , X t - 3 , X t - 4 , X t - 12 ) the ANFIS-WOA model attained root mean square error (RMSE ≈ 0.08 m), mean absolute error (MAE ≈ 0.06 m), and coefficient of determination (R2 ≈ 0.96). Also, the results showed that long-term seasonal memory for this lake is suitable input for lake water-level models so that the long-term dynamic memory of 1-year time series for lake water-level data is the best input for estimating the water level of Titicaca Lake. [ABSTRACT FROM AUTHOR]

In the current study, three machine learning (ML) models, i.e. Gaussian process regression (GPR), generalized regression neural network (GRNN), and multigene genetic programming (MGGP), were developed for predicting the discharge coefficient (Cd) of a radial gate under two different flow conditions, i.e. free and submerged. The modeling development of the flow and geometry input variables for the Cd was determined based on statistical correlations. We also performed a sensitivity analysis of the input variables for the Cd. The modeling results indicated that the developed ML models attained acceptable predictable performance; however, the prediction accuracy of the models was better under the free flow condition. In quantitative terms, the minimum root mean square error (RMSE) value was 0.010 using the GPR model and 0.019 using the MGGP model for the submerged and free flow conditions, respectively. The sensitivity analysis evidenced that the ratio of the gate opening height to the depth of water in the upstream (W:Yo) was the influential variable for the Cd under the free flow condition, whereas the ratio of the depth of water in the upstream to the depth of water in downstream (Yo:YT) was the influential variable for the Cd under the submerged flow condition. [ABSTRACT FROM AUTHOR]

Gaseous pollutants such as hydrogen gas (H2) are emitted in daily human activities. They have been massively studied owing to their high explosivity and widespread usage in many domains. The current research is designed to analyse optical fiber-based H2 gas sensors by incorporating palladium/graphene oxide (Pd/GO) nanocomposite coating as sensing layers. The fabricated multimode silica fiber (MMF) sensors were used as a transducing platform. The tapering process is essential to improve the sensitivity to the environment through the interaction of the evanescent field over the area of the tapered surface area. Several characterization methods including FESEM, EDX, AFM, and XRD were adopted to examine the structure properties of the materials and achieve more understandable facts about their functional performance of the optical sensor. Characterisation results demonstrated structures with a higher surface for analyte gas reaction to the optical sensor performance. Results indicated an observed increment in the Pd/GO nanocomposite-based sensor responses subjected to the H2 concentrations increased from 0.125% to 2.00%. The achieved sensitivities were 33.22/vol% with a response time of 48 s and recovery time of 7 min. The developed optical fiber sensors achieved excellent selectivity and stability toward H2 gas upon exposure to other gases such as ammonia and methane. [ABSTRACT FROM AUTHOR]

Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km2, 15.47 km2, and 9.86 km2 for vegetation, agricultural land, and grassland, respectively. Meanwhile, there is an increase of 35.69 km2 of the built-up area from the year 1990 to 2020. The highest LST has increased by 11.55 °C, while the lowest LST increased by 8.35 °C from 1990 to 2020. The correlation analyses showed negative relationship between LST and vegetation index, while positive correlation was observed for built-up index. Hotspot maps have identified the spatio-temporal thermal variations in Mohanpur, Lohat, Ramnagar, Madhabpur, and Hansdiha where these cities are mostly affected by the urban expansion and industrialization developments. This study will be helpful to urban planners, stakeholders, and administrators for monitoring the anthropological activities and thus ensuring a sustainable urban development. [ABSTRACT FROM AUTHOR]

This study examined landslide susceptibility, an increasingly common problem in mountainous regions across the world as a result of urbanization, deforestation, and various natural processes. The Rangit River watershed in Sikkim Himalaya is one of the most landslide-prone areas in India. The main objective of this study was to produce landslide susceptibility maps of the Rangit River watershed using novel ensembles of random forest tree (RFT) with support vector machine (RFT-SVM), artificial neural network (RFT-ANN), naïve Bayes tree (RFT-NBT), and logistic model tree (RFT-LMT). An inventory of landslides was created using historical landslide data, government and scientific studies, and Google Earth's high-resolution satellite images. The landslide/non-landslide locations were split 70/30 for training and validating the models, respectively. Eleven landslide conditioning factors were selected based on their predictive ability, determined using the information gain method, and each factor's importance was derived. A landslide susceptibility index was then estimated by weighted overlay using a model builder in a GIS (Geographic Information System) environment. Based on the area under the curve and statistical metrics, RFT-LMT was identified as the best model. The results showed that approximately 40% of the Rangit River watershed has high to very high susceptibility to landslides. This study's findings will be useful for policy-makers and land use planners in managing and mitigating future landslides in the study area. [ABSTRACT FROM AUTHOR]

Rock mass deformation modulus (Em) is a key parameter that is needed to be determined when designing surface or underground rock engineering constructions. It is not easy to determine the deformability level of jointed rock mass at the laboratory; thus, researchers have suggested different in-situ test methods. Today, they are the best methods; though, they have their own problems: they are too costly and time-consuming. Addressing such difficulties, the present study offers three advanced and efficient machine-learning methods for the prediction of Em. The proposed models were based on three optimized cascaded forward neural network (CFNN) using the Levenberg–Marquardt algorithm (LMA), Bayesian regularization (BR), and scaled conjugate gradient (SCG). The performance of the proposed models was evaluated through statistical criteria including coefficient of determination (R2) and root mean square error (RMSE). The computational results showed that the developed CFNN-LMA model produced better results than other CFNN-SCG and CFNN-BR models in predicting the Em. In this regard, the R2 and RMSE values obtained from CFNN-LMA, CFNN-SCG, and CFNN-BR models were equal to (0.984 and 1.927), (0.945 and 2.717), and (0.904 and 3.635), respectively. In addition, a sensitivity analysis was performed through the relevancy factor and according to its results, the uniaxial compressive strength (UCS) was the most impacting parameters on Em. [ABSTRACT FROM AUTHOR]

Urban areas are mostly heterogeneous due to settlements and vegetation including forests, water bodies and many other land use and land cover (LULC) classes. Due to the overwhelming population pressure, urbanization, industrial works and transportation systems, urban areas have been suffering from a deficiency of green spaces, which leads to an increase in the variation of temperature in urban areas. This study investigates the conceptual framework design towards urban green space (UGS) and thermal variability over Kolkata and Howrah city using advanced remote sensing (RS) and geospatial methods. The low green space is located in the highly built-up area, which is influenced by thermal variations. Therefore, the heat stress index showed a high area located within the central, north, northwestern and some parts of the southern areas. The vegetated areas decreased by 8.62% during the ten years studied and the other land uses increased by 11.23%. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) showed significant changes with R2 values between 0.48 (2010) and 0.23 (2020), respectively. The correlation among the LST and the normalized difference built-up index (NDBI) showed a notable level of change with R2 values between 0.38 (2010) and 0.61 (2020), respectively. The results are expected to contribute significantly towards urban development and planning, policymaking and support for key stakeholders responsible for the sustainable urban planning procedures and processes. [ABSTRACT FROM AUTHOR]

In this applied research work, the risk of rock instability in the Aqabat Al-Sulbat road section located in the north-west area of Aseer Province in Saudi Arabia was evaluated, and the primary natural trigger factors of rock slope instability on further environmental components (rock slope stability, road network, and urban areas) were estimated using satellite images (Landsat8), digital terrain models, and geoprocessing in geographical information systems software (classification, overlapping algorithms and production thematic mapping in Arctoolbox). Additionally, field geotechnical investigations testing and over-coring drilling sampling allowed the characterization of the section of road in terms of geological structure and environmental components (geology, morphology, road network, lineaments, and hydrology). As a result, rock slope instability vulnerability mapping was simulated using satellite imagery and geographical information systems (GIS) and ranking natural trigger factors using the combined fuzzy Delphi analytical hierarchic process with the technique for order performance by similarity to ideal solution (TOPSIS) as multiple-criteria decision-making (MCDM) techniques. Additionally, many rock layer discontinuity stations were implemented to evaluate rock slope instabilities, and these were visualized using the Dips program and combined with modeling using 3DEC software to predict rock slope failure based on the distinct element method (DEM) at a small scale. Thereafter, safety factors were computed depending on these previous geospatial data. Finally, vulnerability index mapping was combined with rock instability risk mapping for the Aqabat Al-Sulbat road. Within the framework of sustainable development, these results can be used to inform the urban planning of the municipality of Aseer Province. [ABSTRACT FROM AUTHOR]

Coastal vulnerability assessment is the key to coastal management and sustainable development. Sea level rise (SLR) and anthropogenic activities have triggered more extreme climatic events and made the coastal region vulnerable in recent decades. Many parts of the world also noticed increased sediment deposition, tidal effects, and changes in the shoreline. Farasan Island, located in the south-eastern part of Saudi Arabia, experienced changes in sediment deposition from the Red Sea in recent years. This study used Digital Shoreline Analysis System (DSAS) to delineate the shoreline changes of Farasan Island during 1975–2020. Multi-temporal Landsat data and DSAS were used for shoreline calculation based on endpoint rate (EPR) and linear regression. Results revealed an increase in vegetation area on the island by 17.18 km2 during 1975–1989 and then a decrease by 69.85 km2 during 1990–2020. The built-up land increased by 5.69 km2 over the study period to accommodate the population growth. The annual temperature showed an increase at a rate of 0.196 °C/year. The sea-level rise caused a shift in the island's shoreline and caused a reduction of land by 80.86 km2 during 1975–2020. The highly influenced areas by the environmental changes were the north, central, northwest, southwest, and northeast parts of the island. Urban expansion and sea-level rise gradually influence the island ecosystem, which needs proper attention, management, policies, and awareness planning to protect the environment of Farasan Island. Also, the study's findings could help develop new strategies and plan climate change adaptation. [ABSTRACT FROM AUTHOR]

The study aimed to determine Aluminum sludge composition and structure for its valorisation as an alternative natural material for heavy metals removal from wastewater for further reuse as treated water in different applications. The study was conducted to investigate the introduction of Al-bearing sludge composition. The physical and chemical properties were examined using X-ray diffraction tests (XRD), scanning electron microscope tests (SEM), Fourier-transform infrared tests (FTIR), and Brunauer-Emmett-Teller tests (BET). Furthermore, the heavy metal concentrations of synthetic wastewater were measured using the spectrophotometry method. The experimental procedure is based on testing different pH limits and amounts of aluminum sludge to find the optimum conditions for copper (Cu) and zinc (Zn) removal. The results demonstrated a high removal efficiency where its value reached up to 97.4% and 96.6% for Zn and Cu, respectively, in an acidic medium (pH = 6) using a relatively high amount of sludge (1400 mg). Nevertheless, a low efficiency was obtained in the strongly acidic medium (pH = 4) and a smaller sludge amount of about 480 mg. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the hydro-chemical characteristics of Ouargla, Algeria basin groundwaters harvested from the Mio Pliocene aquifer. The study covered 70 samples; the physical parameters, potential of hydrogen (pH), and electrical conductivity EC μS.cm−1 were determined in situ, using a multiparameter; the laboratory analysis included dry residuals DR (mg/L), calcium Ca2+ (mg/L), magnesium Mg2+ (mg/L), sodium Na+ (mg/L), potassium K+ (mg/L), bicarbonates HCO3− (mg/L), sulfates SO42− (mg/L), and chloride Cl− (mg/L). The piper diagram shows that the Ouargla basin ground waters divided into two facies, sodic chlorinated in 93% and sodic sulphated in 7% of samples. The United States Salinity Laboratory Staff (USSL) diagram was used to detect the suitability of groundwater in irrigation where the results show that the groundwater was classed into two classes, poor water (C4 S4) and bad water (C4 S4). Furthermore, indices such as the Kelly index (KI), sodium adsorption ratio (SAR), sodium solubility percentage (Na%), and magnesium hazards (MH) confirm the negative effect of groundwater on soil permeability in 96%, 80%, 89%, and 53% of samples. The permeability index (PI) shows that the analyzed samples were considered as doubtful (71%) and safe (29%), otherwise there is no risk related to residual sodium carbonate (RSC). The geo-spatial distribution of deferent indices shows that all the study area has poor groundwater for irrigation, except the south-west part, where the groundwaters of this sub-area do not form a problem related to RSC. [ABSTRACT FROM AUTHOR]

This study examines land use changes and evaluates the past and projected forest carbon sequestration and its valuation in Viti Levu Island, Fiji, through a combination of remote sensing with a geospatial-based modeling approach. Land use classification was performed using Landsat 7 and Landsat 8 imageries of the years 2000 and 2020; then, cellular automata and artificial neural network (CA-ANN) modeling was conducted to predict the land use map of 2040. Carbon sequestration and the economic valuation were estimated using the land use maps of the past, present, and future (2000, 2020, and 2040) within the Integrated Valuation of Ecosystems Trade-off (InVEST) model. The results showed that deforestation occurred during the past two decades, and the forest area was predicted to keep decreasing in 2040, with the major contribution from the conversion to the agricultural area. Local communities' perceptions confirmed that the forest conversion to croplands would persist due to the demand for fertile lands. This study estimated a loss of −7.337 megatonnes of forest carbon (Mt C) with an economic loss of USD −1369.38 million during 2000–2020 due to deforestation. If the business-as-usual scenario does not change in the near future, a potential carbon loss of −7.959 Mt C is predicted in the upcoming 20 years. The predicted results can be used to assist as a reference in establishing a national baseline and reference level for implementing the REDD+ mechanism in Fiji and sustainably managing the limited pristine forest by implementing forest-related programs. [ABSTRACT FROM AUTHOR]

This study appraised and compared the performance of process-based hydrological SWAT (soil and water assessment tool) with a machine learning-based multi-layer perceptron (MLP) models for simulating streamflow in the Upper Indus Basin. The study period ranges from 1998 to 2013, where SWAT and MLP models were calibrated/trained and validated/tested for multiple sites during 1998–2005 and 2006–2013, respectively. The performance of both models was evaluated using nash–sutcliffe efficiency (NSE), coefficient of determination (R2), Percent BIAS (PBIAS), and mean absolute percentage error (MAPE). Results illustrated the relatively poor performance of the SWAT model as compared with the MLP model. NSE, PBIAS, R2, and MAPE for SWAT (MLP) models during calibration ranged from the minimum of 0.81 (0.90), 3.49 (0.02), 0.80 (0.25) and 7.61 (0.01) to the maximum of 0.86 (0.99), 9.84 (0.12), 0.87 (0.99), and 15.71 (0.267), respectively. The poor performance of SWAT compared with MLP might be influenced by several factors, including the selection of sensitive parameters, selection of snow specific sensitive parameters that might not represent actual snow conditions, potential limitations of the SCS-CN method used to simulate streamflow, and lack of SWAT ability to capture the hydropeaking in Indus River sub-basins (at Shatial bridge and Bisham Qila). Based on the robust performance of the MLP model, the current study recommends to develop and assess machine learning models and merging the SWAT model with machine learning models. [ABSTRACT FROM AUTHOR]

Support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) are two well-known and powerful artificial intelligence techniques which have been frequently used for hazard mapping. So far, a plethora of hybrid models have been developed using a combination of either the SVR or ANFIS and evolutionary algorithms, but there are only a handful of studies that compare the performance of these models when integrated with evolutionary algorithms, especially in forest fire susceptibility mapping (FFSM). The aim of this study was to compare performance of ANFIS-, and SVR-based evolutionary algorithms, namely, the genetic algorithm (GA) and the shuffled frog-leaping algorithm (SFLA) in FFSM in Ajloun Governorate in Jordan. Accordingly, four hybrid models, SVR-GA, SVR-SFLA, ANFIS-GA, and ANFIS-SFLA, were developed and compared. One hundred and one forest fire locations were used in this study to assess and model susceptibility of forests to fires. The forest fire inventory data were divided into a training data subset (70%) and a testing data subset (30%). Fourteen factors affecting incidence of forest fires were employed as conditioning factors. The area under the receiver operating characteristic (AUROC) curve was used to assess performance of the models in the validation phase. The results revealed that the SVR-based hybrid algorithms had better AUROC values than the ANFIS-based algorithms. Of the four integrated models, the SVR-GA model proved to be the model with the highest accuracy and best performance. It had AUROC values of 0.97 and 0.89 in the training and the testing phases, respectively. [ABSTRACT FROM AUTHOR]

Groundwater in the Touggourt region—or as its named, Oued Righ—in southeastern Algeria, is the only source of irrigation. To assess its suitability for agricultural purposes, we collected 72 samples from wells at this region, physical and chemical measurements were carried out for each water sample, and calculations of the sodium adsorption ratio (SAR), permeability index (PI), soluble sodium percent (SSP), residual sodium carbonate (RSC), magnesium hazard ratio (MHR) and Kelley's ratio (KR) were carried out, as these indices are often used to assess the suitability of groundwater for irrigation uses. Based on the irrigation water quality index (IWQI) values, a spatial distribution map for each parameter using the inverse interpolation technique (IDW) was produced by Geographical Information System (GIS). According to the IWQI map, about 35% of the water samples analyzed fall into the Severe Restriction category (SR), making it unsuitable for irrigation under normal circumstance. Again, the remaining 65% of the groundwater has a high restriction (HR) for use. Groundwater in the study area could be used for irrigation in highly permeable soils where salt-tolerant crops are grown. Adequate drainage and continuous monitoring of water quality are recommended. [ABSTRACT FROM AUTHOR]

The composite shear wall has various merits over the traditional reinforced concrete walls. Thus, several experimental studies have been reported in the literature in order to study the seismic behavior of composite shear walls. However, few numerical investigations were found in the previous literature because of difficulties in the interaction behavior of steel and concrete. This study aimed to present a numerical analysis of smart composite shear walls, which use an infilled steel plate and concrete. The study was carried out using the ANSYS software. The mechanical mechanisms between the web plate and concrete were investigated thoroughly. The results obtained from the finite element (FE) analysis show excellent agreement with the experimental test results in terms of the hysteresis curves, failure behavior, ultimate strength, initial stiffness, and ductility. The present numerical investigations were focused on the effects of the gap, thickness of infill steel plate, thickness of the concrete wall, and distance between shear studs on the composite steel plate shear wall (CSPSW) behavior. The results indicate that increasing the gap between steel plate and concrete wall from 0 mm to 40 mm improved the stiffness by 18% as compared to the reference model, which led to delay failures of this model. Expanding the infill steel plate thickness to 12 mm enhanced the stiffness and energy absorption with a ratio of 95% and 58%, respectively. This resulted in a gradual drop in the strength capacity of this model. Meanwhile, increasing concrete wall thickness to 150 mm enhanced the ductility and energy absorption with a ratio of 52% and 32%, respectively, which led to restricting the model and reduced lateral offset. Changing the distance between shear studs from 20% to 25% enhanced the ductility and energy absorption by about 66% and 32%, respectively. [ABSTRACT FROM AUTHOR]

Drought is often one of environmental disasters replicated in Morocco. It is the result of climate variations and human activity that has affected different sectors (water resources, agriculture, ecology, and socio-economy, etc.). The normalized water differential index (NDWI) is a type of spectral water analysis based on one green band and one NIR-band representation. The NDWI was effectively used to gather information around water bodies from remote sensing data. The study area for this work is the Idriss 1st Dam in northeast Morocco, situated downstream from the drainage of the Inaouene River. This basin is mostly affected by drought risk, which will evaluate by calculating NDWI index of image time series, based on Sentinel-2 (2015 to 2020), Landsat-8 (2013 to 2020) and Google Earth Engine (2013 to 2020) as a data processing tool. For the study area drought monitoring, DrinC software is used to calculate the Standardized Precipitation Index (SPI) and the Stream-flow Drought Index (SDI).The result of this paper is mapping water bodies of Idriss 1st Dam, and evaluated drought risk severity and frequency with a high precision. [ABSTRACT FROM AUTHOR]

Extreme climatic conditions and natural hazard-related phenomenon have been affecting coastal regions and riverine areas. Floods, cyclones, and climate change phenomena have hammered the natural environment and increased the land dynamic, socio-economic vulnerability, and food scarcity. Saltwater intrusion has also triggered cropland vulnerability and, therefore, increased the area of inland brackish water fishery. The cropland area has decreased due to low soil fertility; around 252.06 km2 of cropland area has been lost, and 326.58 km2 of water bodies or inland fishery area has been added in just thirty years in the selected blocks of the North 24 Parganas district, West Bengal, India. After saltwater intrusion, soil fertility appears to have been decreased and crop production has been greatly reduced. The cropland areas were 586.52 km2 (1990), 419.92 km2 (2000), 361.67 km2 (2010) and 334.46 km2 (2020). Gradually the water body areas were increased 156.21 km2 (1990), 328.15 km2 (2000), 397.77 km2 (2010) and 482.78 km2 (2020). The vegetated land area also decreased due to it being converted into inland fishery areas, and around 79.15 km2 were degraded during the last thirty years. The super cyclone Aila, along with other super cyclones and other environmental stresses, like water-logging, soil salinity, and irrigation water scarcity were the reasons for the development of the new fishery areas in the selected blocks. There is a need for proper planning for sustainable development of this area. [ABSTRACT FROM AUTHOR]

In recent years, steel-concrete composite shear walls have been widely used in enormous high-rise buildings. Due to their high strength and ductility, enhanced stiffness, stable cycle characteristics and large energy absorption, such walls can be adopted in auxiliary buildings, surrounding the reactor containment structure of nuclear power plants to resist lateral forces induced by heavy winds and severe earthquakes. The current study aims to investigate the seismic behaviour of composite shear walls and evaluate their performance in comparison with traditional reinforced concrete (RC) walls when subjected to cyclic loading. A three-dimensional finite element model is developed using ANSYS by emphasising constitutive material modelling and element type to represent the real physical behaviour of complex shear wall structures. The analysis escalates with parametric variation in reinforcement ratio, compressive strength of the concrete wall, layout of shear stud and yield stress of infill steel plate. The modelling details of structural components, contact conditions between steel and concrete, associated boundary conditions and constitutive relationships for the cyclic loading are explained. The findings of this study showed that an up to 3.5% increase in the reinforcement ratio enhanced the ductility and energy absorption with a ratio of 37% and 38%, respectively. Moreover, increasing the concrete strength up to 55 MPa enhanced the ductility and energy absorption with ratios of 51% and 38%, respectively. Thus, this improves the contribution of concrete strength, while increasing the yield stress of steel plate (to 380 MPa) enhanced the ductility (by a ratio of 66%) compared with the reference model. The present numerical research shows that the compressive strength of the concrete wall, reinforcement ratio, layout of shear stud and yield stress of infill steel plate significantly affect ductility and energy absorption. Moreover, this offers a possibility for improving the shear wall's capacity, which is more important. [ABSTRACT FROM AUTHOR]

The quality of groundwater in the study watershed has worsened because of industrial effluents and residential wastes from the urbanized cities; therefore, there is an important need to explore the aquifer vulnerability to pollution for sustainable groundwater management in the Irrigated Indus Basin (IIB). This study proposed a novel methodology to quantify groundwater vulnerability using two fully independent methodologies: the first by reintroducing an improved recharge factor (R) map and the second by incorporating three different weight and rating schemes into a traditional DRASTIC framework to improve the performance of the DRASTIC approach. In the current study, we composed a recharge map from Soil and Water Assessment Tool (SWAT) output (namely SWAT recharge map) with a drainage density map to retrieve an improved composite recharge map (SWAT-CRM). SWAT-CRM along with other thematic layers was combined using weightage overlay analysis to prepare the maps of groundwater vulnerability index (VI). The weight scale (w) and rating scale (r) were assigned based on a survey of available literature, and we then amended them using the analytical hierarchy process (AHP) and a probability frequency ratio (PFR) technique. Results depicted that the region under high groundwater vulnerability was found to be 5–22% using traditional recharge maps, while those are 9–23% using improved SWAT-CRM. The area under the curve (AUC) revealed that groundwater vulnerability zones predicted with SWAT-CRM outperformed the DRASTIC model applied with the traditional recharge map. Groundwater electrical conductivity (EC) was>2500 mS/cm in the high groundwater vulnerability zones, while it was <1000 mS/cm in the low groundwater vulnerability zones. The outcomes of this study can be used to improve the sustainability of the groundwater resources in IIB through proper land-use management practices. [ABSTRACT FROM AUTHOR]

This study, for the first time, assesses the impact of critical environmental factors on groundwater using Bayesian network (BN) integrated with analytical hierarchy process (AHP) and develops a groundwater vulnerability map. The considered environmental factors are divided into the following categories: physical (rainfall, temperature (Tmax/Tmin), relative humidity (RHmax/RHmin)), water use and demand (surface water availability (SW) and number of tubewells (NTW)), agriculture and land use (total irrigated area (TIA), total cropped area (TCA), and total area sown (TAS)), and population. Results show a negative relationship of rainfall, RHmax/RHmin, and SW and a positive relationship of the remaining variables with groundwater. Elasticities demonstrate that a 1% change in SW (rainfall), major contributors, results in a decrease by 0.64% (0.55%) in groundwater in Bahawalpur (Multan). A 1% change in population (NTW), major consumers, results in an increase by 0.74% (0.70%) in depth to water table (DWT) across Jhang (Khanewal). The vulnerability map shows that high and very high vulnerability classes account for more than 50% of the total area of Punjab. [ABSTRACT FROM AUTHOR]

The precision of reference evapotranspiration (ETo) predictions would vary, depending on the adopted empirical method and the availability of meteorological data. This study aims to enhance the prediction accuracy of ETo using the high-order response surface method (HO-RSM). Daily scale climatological information are used to build the predictive model including maximum temperature (Tmax), maximum humidity (Hmax), wind speed (WS), solar radiation (SR), and vapor pressure deficit (VPD), which are obtained from three observation stations in Burkina Faso, West Africa. Ten models corresponding to ten different input combination sets are evaluated for variability influence by comparing the predicted ETo with the observed ETo. The models presented a similar performance at both Gaoua and Boromo stations with the determination coefficient (R2) and root mean square error (RMSE) values ranging between 0.6831–0.9966 (0.0622–0.5065) and 0.7237–0.9948 (0.0722–0.4942), respectively. As for the Dori station, the models showed a lower performance with R2 (RMSE) values ranging between 0.2068 and 0.5229 (0.8292–1.0051), which may be due to the insufficient input variables or the requirement of higher order in RSM modeling for this station. Results also showed that the M10 model that includes all five input variables performed the best at three stations, with respect to the statistical performance. This is followed by the M7 model, which excluded the Hmax in the prediction, suggesting that Hmax has the least influence on the ETo prediction among all the input variables. The insignificant trend in selecting the optimum order of the RSM also showed that HO-RSM is case sensitive and hence precautions are required for generalizing model applications. [ABSTRACT FROM AUTHOR]

Many regions of Iraq were suffering from (meteorological and agricultural) drought. Middle Euphrates region is one of these regions. Therefore, this study carried out to monitor this phenomenon in this region during period from 1988 to 2018. The Reconnaissance Drought Index (RDI) and the Vegetation Condition Index (VCI) have been utilized for monitoring the meteorological and agricultural droughts, respectively. The final results showed that the overall drought percentage in the study area had been increased compared with no drought percentage. The percentage of the no drought area was ranged from 8% (in 1988 and 2000) to 17% (in 2018). The severe and extreme drought classes recorded a higher ratio in the area followed by moderate and mild droughts. There is a high level of the dry season in the region, especially in its southern and western parts. The reason for drought particularly in these parts is due to the geological composition of its soils, where it consists sandy soils that are consistently subjected to erosion by the wind. Additionally, the high porosity of these soils adversely influenced on their water maintenance limit particularly water of precipitation. In addition to the absence of a source of surface water in these parts of the region and its dependence on the water of some wells with high salinity and unsuitable for agriculture. [ABSTRACT FROM AUTHOR]

Morphometric analysis is a pertinent scientific approach in any hydrological analysis, and it is necessary in the progress and management of drainage basin. Identification of areas at risk of erosion, and the prioritization of 48 sub-watersheds of Inaouene basin, was done by using linear, relief and areal aspects of watershed. The research carried out the use of geographic information system spatial data. The linear aspects include stream number, stream sequence, stream length, and bifurcation ratio, mean length of stream order, stream length ratio, mean stream length ratio, and form factor. The areal aspect includes frequency of stream, drainage density, texture ratio, channel length constant, and overland flow maintenance length. Ultimately, the relief dimensions included relief proportion, relief and ruggedness number. The array of compound (Cp) values computed allow us to set the priority ranks and classify the sub-watershed into three priority ranks groups: low, moderate, and high priority. Such morphometric analyses can be used therefore as a watershed erosion status estimator to prioritize land and water conservation initiatives and natural resources management. [ABSTRACT FROM AUTHOR]

This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 dark target (DT), deep blue (DB), and multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) during the COVID-19 lockdown period (February–May 2020) compared to the pre-COVID-19 period (February–May 2019). Validation of AOD retrievals was conducted against AErosol RObotic NETwork (AERONET) Version 3 Level 1.5 AOD data obtained from three sites located in urban (Beijing_CAMS and Beijing_RADI) and suburban (XiangHe) areas of China. The results show the poor performance of the DT and DB algorithms compared to the MAIAC algorithm, which performed better during the lockdown period. Overall, all MODIS algorithms overestimated the AOD and showed higher positive bias under high aerosol loading conditions during lockdown than during prelockdown. This is mainly attributed to the overestimation of the aerosol single-scattering albedo (SSA), which was found higher during lockdown than during the same period in 2019. [ABSTRACT FROM AUTHOR]

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

The paramo, plays an important role in our ecosystems as They balance the water resources and can retain substantial quantities of carbon. This research was carried out in the province of Tungurahua, specifically the Quero district. The aim is to develop a classification of the land use land cover (LULC) in the paramo using satellite imagery using several classifiers and determine which one obtains the best performance, for which three different approaches were applied: Pixel-Based Image Analysis (PBIA), Geographic Object-Based Image Analysis (GEOBIA), and a Deep Neural Network (DNN). Various parameters were used, such as the Normalized Difference Vegetation Index (NDVI), the Bare Soil Index (BSI), texture, altitude, and slope. Seven classes were used: paramo, pasture, crops, herbaceous vegetation, urban, shrubrainland, and forestry plantations. The data was obtained with the help of onsite technical experts, using geo-referencing and reference maps. Among the models used the highest-ranked was DNN with an overall precision of 87.43%, while for the paramo class specifically, GEOBIA reached a precision of 95%. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions, groundwater is considered being the most available natural resources for different water use. However, it is being limited in quantity. As such, its sustainable development and managementent depends on based on various criteria (e.g. climatic conditions, scale, aquifer properties, etc.). This study presents three multi-criteria index approaches (Analytic Hierarchy Process (AHP), Best–Worst Method (BWM), and Full Consistency Method (FUCOM) to classify groundwater potential maps in the Sarakhs Plain in North-east Iran. In this study, 10 parameters (layers) that affect groundwater potential recharge mapping (GPRM) are used using ArcGIS10.2. These layers includeground surface elevation, surface slope, aspect, relative slope position (RSP), plan curvature, topographic wetness index (TWI), terrain ruggedness index (TRI), drainage density, landuse, and lithology. These layers and their features were assigned properweights based on the conceptual frameworks of AHP, BWM, and FUCOM techniques, and then using a weighted overlay summation process (WOSP), final maps of groundwater potential in Sarakhs plain are obtained. The developed groundwater potential maps are classified into four classes, including low, medium, high, and very-high. The results show that among the 10 driving parameters, land use, and lithology have the highest importance and the surface slope has the lowest importance in the mapping of groundwater potential recharge. The best groundwater potential zones are concentrated in northeast and southeast, central parts, and a few parts in the areas of the western regionof the Sarakhs plain due to its nearly gentle slopes with quaternary alluvial and agriculture land and lower drainage density. The obtained results are of high value for decision-makers in the Sarakhs plain in specific and for entire Iran in general to apply sustainable groundwater utilization plans. [ABSTRACT FROM AUTHOR]

Peatlands in tropical regions like Indonesia are undergoing irreversible subsidence due to changes in land use (e.g., deforestation) and land management practices (e.g., drainage alteration), resulting in massive amounts of soil carbon loss. Several satellite‐borne synthetic aperture radar (SAR) sensors are operating concurrently at different frequencies, providing potentially useful data for monitoring surface motion over tropical peatlands. This study focused on the capability of C‐band (SENTINEL‐1) and L‐band (PALSAR‐2) SAR data to monitor the surface changes in the tropical peatlands area of Bengkalis Island, Indonesia, by applying time‐series interferometric SAR (InSAR) with the small baseline subset (SBAS) technique. The average vertical velocity measured by SENTINEL‐1 and PALSAR‐2 for the period of 2018–2019 was −1.41 and −2.65 cm yr−1, respectively. We also explored the potential of groundwater level (GWL) data converted to vertical displacement for validating SBAS InSAR. PALSAR‐2 performed the best, exhibiting lower RMSE values for each land use compared to SENTINEL‐1, with an overall RMSE of 1.383 and 1.988 cm yr−1, respectively. Also, the subsidence rates of SENTINEL‐1 were underestimated, showing a significantly lower mean subsidence difference (0.96 cm yr−1) than the reference. Our GWL‐based subsidence data offered an alternative validation method for InSAR‐based subsidence estimation. Therefore, the integration of time‐series InSAR and GWL data can provide crucial information for monitoring the degradation of tropical peatlands. [ABSTRACT FROM AUTHOR]

Runoff–sediment process modeling is highly variable and nonlinear in nature. For sediment yield prediction, the difficulty of rainfall–runoff–sediment yield hydrological processes remains challenging. The present study uses a simple nonlinear dynamic (NLD) model to predict daily sediment yields, taking into account the degree of daily–sediment yield in catchment areas, and its findings were compared to three widely used models including artificial neural networks (ANN), support vector machine (SVM), and gene expression programming (GEP). The daily measured discharge–sediment data for 25 years were obtained from Shakkar Watershed; Central India as in the current study. The coefficient of correlation (CC), Nash-Sutcliff (NS), and root-mean-square error (RMSE) were employed to assess the performance of the models. The results show that the NLD model was found better than ANN, SVM, and GEP model. These models had correlation coefficient (CC = 0.975, 0.887, 0.843, and 0.901), root-mean-square error (RMSE = 0.748, 1.751, 1.961, and 1.545), and Nash–Sutcliffe efficiency (0.952, 0.784, 0.673, and 0.814) correspondingly. Hence, the NLD model can be used for predicting sediment. In order to implement appropriate measures of soil conservation in the watershed to reduce the sediment load in the river, predicting the sediment yield is very necessary to maximize the life of the structure. [ABSTRACT FROM AUTHOR]

Soil erosion causes significant damage to humans by reducing soil productivity and filling reservoirs from sediment deposition in Narmada Basin, India; hence, it is important to recognize soil erosion prone areas for preventive steps in this basin. In this research, prioritization of sub-watersheds of Narmada Basin has been done using game theory-based approaches such as Condorcet and Fallback bargaining. For this purpose, Digital Elevation Model (DEM) generated by Shuttle Radar Topography Mission (SRTM) was used to extract and analyze 12 morphometric parameters including linear, aerial, and relief parameters. Based on the Condorcet and Fallback bargaining methods, the Mohgaon watershed came at the first priority ranking, that means it's the most vulnerable watershed from the point of soil erosion (SE). Game theory was successfully implemented for prioritizing watersheds in term of SE. The findings showed that morphometric parameters and game theory approach have a high efficiency in recognizing areas that are vulnerable to erosion. [ABSTRACT FROM AUTHOR]

The paper is concern to the approximate analytical solution of K(2,2) using the multistage homotopy asymptotic method which are used in modern physics and engineering. The suggested algorithm is an accurate, effective, and simple to-utilize semi-analytic tool for non-linear problems, and in this manner the current investigation highlights the efficiency and accuracy of the method for the solution of non-linear PDE for large time span. Numerical comparison with the variational iteration method and with homotopy asymptotic method shows the efficacy and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we present new necessary and sufficient conditions for the oscillation of a class of second-order neutral delay impulsive differential equations. Our oscillation results complement, simplify and improve recent results on oscillation theory of this type of nonlinear neutral impulsive differential equations that appear in the literature. An example is provided to illustrate the value of the main results. [ABSTRACT FROM AUTHOR]

The study aims to evaluate the long-term accuracy of global precipitation (Climate Prediction Center (CPC) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR)) along with raingauge datasets at multiple temporal scales in the Vu Gia Thu Bon basin, Vietnam. Since there are few rainfall stations in this basin, it is important to validate multisource data for multiple purposes. This is the first time that a lumped hydrological model (i.e. Probability Distributed Moisture (PDM)) has been used for this basin. Various statistical indicators, including the correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), percent bias (BIAS) and Taylor diagram, were used to evaluate the applicability of the global precipitation data and the PDM model. The precipitation datasets showed a good correlation with the raingauge rainfall data. In contrast, CPC underestimates while PERSIANN-CDR overestimates the raingauge rainfall. In general, PERSIANN-CDR performed slightly better than CPC. The daily streamflow simulation driven by PDM and all data sources underestimates the actual flow. [ABSTRACT FROM AUTHOR]

In this work, we prove some new oscillation theorems for second-order neutral delay differential equations of the form (a (ξ) ((v (ξ) + b (ξ) v (ϑ (ξ))) ′)) ′ + c (ξ) G 1 (v (κ (ξ))) + d (ξ) G 2 (v (ς (ξ))) = 0 under canonical and non-canonical operators, that is, ∫ ξ 0 ∞ d ξ a (ξ) = ∞ and ∫ ξ 0 ∞ d ξ a (ξ) < ∞ . We use the Riccati transformation to prove our main results. Furthermore, some examples are provided to show the effectiveness and feasibility of the main results. [ABSTRACT FROM AUTHOR]

In this work, we aimed to obtain sufficient and necessary conditions for the oscillatory or asymptotic behavior of an impulsive differential system. It is easy to notice that most works that study the oscillation are concerned only with sufficient conditions and without impulses, so our results extend and complement previous results in the literature. Further, we provide two examples to illustrate the main results. [ABSTRACT FROM AUTHOR]

In this paper, we study the oscillatory and asymptotic behavior of a class of first-order neutral delay impulsive differential systems and establish some new sufficient conditions for oscillation and sufficient and necessary conditions for the asymptotic behavior of the same impulsive differential system. To prove the necessary part of the theorem for asymptotic behavior, we use the Banach fixed point theorem and the Knaster–Tarski fixed point theorem. In the conclusion section, we mention the future scope of this study. Finally, two examples are provided to show the defectiveness and feasibility of the main results. [ABSTRACT FROM AUTHOR]

In this present study, pure and Ni2+ (3, 7, and 10% mol.)-substituted SnO2 nanoparticles (NPs) were synthesized by sol-gel method. The prepared samples were characterized by powder XRD, FT-IR, FE-SEM, HR-TEM, EDX, UV-visible spectra, room temperature (RT) photoluminescence (PL) spectroscopy, and VSM techniques. XRD study revealed the polycrystalline tetragonal rutile structure of pure and Ni2+-substituted SnO2 NPs. Crystallite size of pure and Ni2+-substituted SnO2 NPs were found to be 26 to 34 nm in range. FT-IR study confirmed the presence of metal-oxide (M-O) bond vibrations for pure and Ni-substituted SnO2 NPs. FE-SEM, EDX, HR-TEM, and SAED pattern indicated that pure and Ni2+-substituted SnO2 NPs are well-defined formation of spherical-sized nanocrystallites, with diameter of about 10–40 nm of grain size. UV-Vis spectrum specified a sharp absorption peak ~ 425 nm representing the band to band transition. RT-PL study showed the peak ~ 467 nm for all samples, indicating the band gap of 2.65 eV. The higher value of Ms is found to be 0.0429 emu/g for Ni-substituted SnO2 NPs. Magnetic study revealed that the Ni-substituted SnO2 NPs presented ferromagnetism. Photocatalytic degradation (PCD) of methylene blue (MB) was analyzed using photo-catalytic reactor and obtained the maximum of PCD efficiency of 3% Ni-substituted SnO2 NPs under the visible light irradiation. [ABSTRACT FROM AUTHOR]

Precise knowledge of the soil organic carbon (SOC) stocks under various land uses is needed to meet the Kyoto Protocol and for the sustainability of natural resources. The purpose of the present study was to (1) gauge the depth and spatial distribution of the soil bulk density (BD), soil organic carbon (OC) stocks, and soil organic matter (OM) among the various land uses in the northern Nile Delta in Egypt; (2) estimate the soil carbon sequestration rate (CSR) under different land uses in the region; and (3) establish baseline data for SOC stocks in future studies on the dynamics of SOC. The study area was divided into ten sampling zones to represent each land use in the northern Nile Delta. Each sampling zone was further divided into four sampling sites to represent virgin lands and fish farms, and twelve sites were used to represent three crop types and four cultivation histories. The crops included clover, (Trifolium alexandrinum L.), sugar beet (Beta vulgaris L.), and rice, (Oryza sativa L.) and the years spanned were 5, 15, 30 and 50. The effects of the crop type on the SBD, SOC content, and SOC stocks were significant. In general, the SOC stocks increase as the number of years of cultivation increased. Thematic maps were produced using Geographical Information Systems (GIS) mapping. The Inverse Distance Weighted (IDW) technique in ArcGIS10.4 software revealed that the spatial pattern of the SBD, OC content, and stocks conformed to the soil analysis results. The SOC stocks of the croplands and fish farms were 1.6 and 1.5 times higher, respectively, compared to those of virgin land. Rice cropland had the lowest SBD (1.34 g cm−3) and the highest OC stocks (7.46 g C kg−1). The conversion of virgin land into croplands or fish farms actively contributed to the carbon storage rate (CSR). [ABSTRACT FROM AUTHOR]

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson's correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and −0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health. [ABSTRACT FROM AUTHOR]

Examining the influence of land use/land cover transformation on meteorological variables has become imperative for maintaining long-term climate sustainability. Rapid growth and haphazard expansion have caused the conversion of prime agricultural land into a built-up area. This study used multitemporal Landsat data to analyze land use/land cover (LULC) changes, and Terra Climate monthly data to examine the impact of land transformation on precipitation, minimum and maximum temperature, wind speed, and soil moisture in the Aurangabad district of Maharashtra state in India during 1999–2019. Multiple linear regression and correlation analysis were performed to determine the association among LULC classes and climatic variables. This study revealed rapid urbanization in the study area over the years. The built-up area, water bodies, and barren lands have recorded a steep rise, while the agricultural area has decreased in the district. Drastic changes were observed in the climatic variables over the years. The precipitation and wind speed have shown decreasing trends during the study period. A positive relationship between soil moisture and agricultural land was found through a correlation analysis. Conspicuous findings about the positive relationship between the agricultural land and maximum temperature need further investigation. A multiple linear regression analysis demonstrated a negative relationship between the built-up area and precipitation. The intensity of the precipitation has reduced as a consequence of the developmental activities in the study area. Moreover, a positive relationship was observed between the built-up area and maximum temperature. Thus, this study calls for policy implications to formulate a futuristic land-use plan considering climate change projection in the district. [ABSTRACT FROM AUTHOR]

In this paper, we study the general solution of the functional equation, which is derived from additive–quartic mappings. In addition, we establish the generalized Hyers–Ulam stability of the additive–quartic functional equation in Banach spaces by using direct and fixed point methods. [ABSTRACT FROM AUTHOR]

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon's entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years. [ABSTRACT FROM AUTHOR]