S patio-temporal pattern of land use and land cover and its effects on land surface temperature using remote sensing and GIS techniques: a case study of Bhubaneswar city,Eastern India (1991–2021)

Urbanization produces substantial land use changes by causing the construction of different urban infrastructures in the city region for habitation, transportation, industry, and other reasons. As a result, it has a significant impact on Land Surface Temperature (LST) by disrupting the surface energy balance. The objective of this paper is to assess the impact of land-use/land-cover (LU/LC) dynamics on urban land surface temperature (LST) of Bhubaneswar City in Eastern India during 30 years (1991–2021) using Landsat data (TM, ETM + , and OLI/TIRS) and machine learning algorithms (MLA). The finding reveals that the mean LST over the entire study domain grows significantly between 1991 and, 2021due to urbanization (β coefficient 0.400, 0.195, 0.07, and 0.06 in 1991, 2001, 2011, and 2021 respectively) and loss of green space (β coefficient − 0.295, − 0.025, − 0.125 and − 0.065 in 1991, 2001, 2011 and 2021 respectively). The highest class recorded for agricultural land (49.60 km², accounting for 33.94% of the total land area) was in 1991 followed by vegetation (41.27 km², 28.19% of the total land area), and built-up land (27.59 km², 18.84% of the total land area). The sharp decline of vegetation cover will continue until 2021 due to increasing built-up areas (r = − 0.531, − 0.329, − 0.538, and − 0.063 in the 1991, 2001, 2011 and 2021 respectively). Built-up land (62.60 km², accounting for 42.76% of the total land area, an increase of 35.01 km² from 1991) as the highest class followed by water bodies (21.57%, 32.60 km² of the land area), and agricultural land (31.57 km², 21.57% of the land area) in 2021. Remote sensing techniques proved to be an important tool to urban planners and policymakers to take adequate steps to promote sustainable development and minimize urbanization influence on LST. Urban green space (UGS) can help improve the overall liveability and environmental sustainability of Bhubaneswar city.

     

Randomized maximum likelihood based posterior sampling

Minimization of a stochastic cost function is commonly used for approximate sampling in high-dimensional Bayesian inverse problems with Gaussian prior distributions and multimodal posterior distributions. The density of the samples generated by minimization is not the desired target density, unless the observation operator is linear, but the distribution of samples is useful as a proposal density for importance sampling or for Markov chain Monte Carlo methods. In this paper, we focus on applications to sampling from multimodal posterior distributions in high dimensions. We first show that sampling from multimodal distributions is improved by computing all critical points instead of only minimizers of the objective function. For applications to high-dimensional geoscience inverse problems, we demonstrate an efficient approximate weighting that uses a low-rank Gauss-Newton approximation of the determinant of the Jacobian. The method is applied to two toy problems with known posterior distributions and a Darcy flow problem with multiple modes in the posterior.

     

Influence of Gassing Agent and Density on Detonation Velocity of Bulk Emulsion Explosives

The demand for coal from surface mining projects are on the higher side like never before for which blasting is the basic unit operation. The explosive plays an important role in blasting and also influence the explosive-rock interaction. The most common explosive type used in surface mines is emulsion explosives. This paper presents the study on the detonation velocity of bulk emulsion explosives due to variation in gassing agent and density. In this study Sodium Nitrite (NaNO₂) has been used as the gas generating additive and the performance of emulsion explosives with different concentrations of gassing agents at different temperatures has been observed. This study was undertaken to also understand the cyclic variation of temperature on gassing kinetics and performance of explosive. The effect of cooling on detonic-behaviour of bulk emulsion explosives has also been studied and presented in this paper.     

Biogeochemical controls of arsenic occurrence and mobility in the Indian Sundarban mangrove ecosystem

This study aims to investigate the control of arsenic distribution by biogeochemical processes in the Indian Sundarban mangrove ecosystem and the importance of this ecosystem as an arsenic source for surrounding coastal water. The As(V)/As(III) ratio was found to be significantly lower in both surface and pore waters compared to sea water, which could be attributed to biogeochemical interconversion of these arsenic forms. The biological uptake of arsenic due to primary and benthic production occurs during the post-monsoon season, and is followed by the release of arsenic during the biochemical degradation and dissolution of plankton in the pre-monsoon season. These results suggest that arsenic is immobilized during incorporation into the arsenic-bearing initial phase, and unlikely to be released into pore water until the complete microbial degradation of arsenic-bearing organic compounds.     

On Methods for In‐Well Nitrate Monitoring Using Optical Sensors

Optical sensors are promising for collecting high resolution in‐well groundwater nitrate monitoring data. Traditional well purging methods are labor intensive, can disturb ambient conditions and yield an unknown blend of groundwater in the samples collected, and obtain samples at a limited temporal resolution (i.e., monthly or seasonally). This study evaluated the Submersible Ultraviolet Nitrate Analyzer (SUNA) for in‐well nitrate monitoring through new applications in shallow overburden and fractured bedrock environments. Results indicated that SUNA nitrate‐N concentration measurements during flow cell testing were strongly correlated (R ² = 0.99) to purged sample concentrations. Vertical profiling of the water column identified distinct zones having different nitrate‐N concentrations in conventional long‐screened overburden wells and open bedrock boreholes. Real‐time remote monitoring revealed dynamic responses in nitrate‐N concentrations following recharge events. The monitoring platform significantly reduced labor requirements for the large amount of data produced. Practitioners should consider using optical sensors for real‐time monitoring if nitrate concentrations are expected to change rapidly, or if a site's physical constraints make traditional sampling programs challenging. This study demonstrates the feasibility of applying the SUNA in shallow overburden and fractured bedrock environments to obtain reliable data, identifies operational challenges encountered, and discusses the range of insights available to groundwater professionals so they will seek to gather high resolution in‐well monitoring data wherever possible.     

GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

Hazards and disasters have always negative impacts on the way of life.Landslide is an overwhelming natural as well as man-made disaster that causes loss of natural resources and human properties throughout theworld.The present study aimed to assess and compare the prediction efficiency of different models in landslide susceptibility in the Kysuca river basin,Slovakia.In this regard,the fuzzy decision-making trial and evaluation laboratory combining with the analytic network process(FDEMATEL-ANP),Na?ve Bayes(NB)classifier,and random forest(RF)classifier were considered.Initially,a landslide inventory map was produced with 2000 landslide and nonlandslide points by randomly dividedwith a ratio of 70%:30%for training and testing,respectively.The geospatial database for assessing the landslide susceptibility was generated with the help of 16 landslide conditioning factors by allowing for topographical,hydrological,lithological,and land cover factors.The ReliefF methodwas considered for determining the significance of selected conditioning factors and inclusion in the model building.Consequently,the landslide susceptibility maps(LSMs)were generated using the FDEMATEL-ANP,Na?ve Bayes(NB)classifier,and random forest(RF)classifier models.Finally,the area under curve(AUC)and different arithmetic evaluation were used for validating and comparing the results and models.The results revealed that random forest(RF)classifier is a promising and optimum model for landslide susceptibility in the study area with a very high value of area under curve(AUC=0.954),lower value of mean absolute error(MAE=0.1238)and root mean square error(RMSE=0.2555),and higher value of Kappa index(K=0.8435)and overall accuracy(OAC=92.2%).     

Fine mode aerosol chemistry over a tropical urban atmosphere: characterization of ionic and carbonaceous species

An extensive aerosol sampling program was conducted during January-December 2006 over Kolkata (22o33?? N and 88o20?? E), a mega-city in eastern India in order to understand the sources, distributions and properties of atmospheric fine mode aerosol (PM_2.5). The primary focus of this study is to determine the relative contribution of natural and anthropogenic as well as local and transported components to the total fine mode aerosol loading and their seasonal distributions over the metropolis. The average concentrations of fine mode aerosol was found to be 71.2?±?25.2???gm^-3 varying between 34.5???gm^-3 in monsoon and 112.6???gm^-3 in winter. The formation pathways of major secondary aerosol components like nitrate and sulphate in different seasons are discussed. A long range transport of dust aerosol from arid and semi-arid regions of western India and beyond was observed during pre-monsoon which significantly enriched the total aerosol concentration. Vehicular emissions, biomass burning and transported dust particles were the major sources of PM_2.5 from local and continental regions whereas sea-salt aerosol was the major source of PM_2.5 from marine source regions.     

Two types of ultrapotassic plutonic rocks in the Bohemian Massif — Coeval intrusions at different crustal levels

We present U–Pb zircon age determinations of two Variscan ultrapotassic plutonic rocks from the Moldanubian Zone (Bohemian Massif). Equant, multifaceted zircons without inherited cores from a two-pyroxene–biotite quartz monzonite of the Jihlava Pluton yielded a precise age of 335.12 ± 0.57 Ma, interpreted as dating magma crystallization. The majority of both tabular and prismatic grains from the amphibole–biotite melagranite (“durbachite”) from the T?ebí? Pluton plot along a discordia intersecting the concordia at 334.8 ± 3.2 Ma; prismatic zircon grains commonly contain inherited cores and yield an upper intercept age of 2.2 Ga, indicating early Proterozoic inheritance. We therefore suggest that both types of the ultrapotassic plutonic rocks from the Bohemian Massif crystallized at ca 335 Ma, and the previously published ages higher than ca 340 Ma for “durbachites” were biased by a small amount of unresolved inheritance. The ultrapotassic magma emplacement in the middle crust was related to rapid exhumation of a deep crustal segment, considered as isothermal decompression between high-pressure (～ 340 Ma) and medium-pressure (～ 333 Ma) stages recorded in granulites. Mineral assemblages as well as external and internal zircon morphology suggest that the Jihlava intrusion was deep and dry, whereas the T?ebí? intrusion was shallow and wet. Low ε_Hf values of zircons (? 4.4 to ? 7.5) in both rock types suggest a similar source with a predominant crustal component. However, inherited grains in the T?ebí? melagranite indicate its contamination with crustal material during emplacement, and thus possibly a slower rate of exhumation and/or of magma ascent through the crust.     

Summer evapotranspiration in western Siberia: a comparison between eddy covariance and Penman method formulations

Evapotranspiration is difficult to quantify because of the many factors and complex processes that influence it. Several empirical methods have been developed over the years to estimate potential evapotranspiration based on easily available parameters. Directly measured data of actual evapotranspiration have been rather sparse in the past and still need to be improved in particular regions like western Siberia. The transition zone between the warm temperate and cold temperate continental climates is very sensitive to climate change, and water stress is an increasingly important issue in these regions with a highly dynamic agricultural activity. So there is a growing need to estimate actual evapotranspiration. Widely usable approximations are needed. In this study, the values of potential evapotranspiration computed with the original version, and eight modifications of the Penman formulation were compared and related to the actual evapotranspiration measured by eddy covariance over a grassland area in western Siberia. The original 1948 and 1963 Penman formulations are best for estimating potential evapotranspiration in the transition zone between the forest steppes and the pre‐taiga. A nearly linear relationship between the potential and actual evapotranspiration was found. A simple modification of the Penman equation (i.e. the multiplication of the result by a factor of 0.47) is suggested for approximating the actual evapotranspiration based on standard meteorological data for the region. The original Penman formulation is most robust and will provide the widest applicability in the future under changing climate and environmental conditions. In this context, it is further recommended not to neglect the ventilation term of the Penman equation, which is often assumed to be negligibly small. A detailed correlation analysis showed that under dry soil conditions, the vegetation largely contributed to the actual evapotranspiration and, in contrast to widely held expectations, that the Penman equation is best adapted to vegetated surfaces. Copyright © 2015 John Wiley & Sons, Ltd.