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.
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.
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. 相似文献
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. 相似文献
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 (R2 = 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. 相似文献
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%). 相似文献
Ongoing climate change and anthropogenic activities are introducing stressors that affect the structure and function of coastal ecosystems. This paper focuses on the fluvial fluxes and estuarine transport of nutrients from a tropical river (Mahanadi River) in Northeastern India and compares select nutrient and water quality parameters between 1983 and 2008. This estuary acts as a perennial source of CO2 with a net annual flux to the atmosphere of about 135 tons. The non‐conservative fluxes showed a net annual removal of 650 and 140 tons of phosphorus and nitrogen from the water column, respectively. Negative biogeochemical feedbacks that decreased the availability of N and P in 2008 relative to 1983 levels indicate major changes in biogeochemical responses towards fluvial fluxes of nutrient. 相似文献
Elevated concentrations of arsenic in the sediment and pore water in the Sundarban wetlands pose an environmental risk. Adsorption
and desorption are hypothesized to be the major processes controlling arsenic retention in surface sediment under oxic/suboxic
condition. This study aims to investigate sorption kinetics of As(III & V) and its feedback to arsenic mobilization in the
mangrove sediment. It ranges from sand to silty clay loam and shows the adsorption of As(III & V) following the Langmuir relation.
Estimates of the maximum adsorption capacity are 59.11 ± 13.26 μg g−1 for As(III) and 58.45 ± 8.75 μg g−1 at 30°C for As(V) in the pH range 4 to 8 and salinity 15–30 psu. Extent of adsorption decreases with increasing pH from 4
to 8 and desorption is the rate-limiting step in the reaction of arsenic with sediment. Arsenic in the sediment could be from
a Himalayan supply and co-deposited organic matter drives its release from the sediment. Arsenic concentration in the sediment
is well below its maximum absorption capacity, suggesting the release of sorbed arsenic in pore water by the microbial oxidation
of organic matter in the sediment with less feedback of adsorption. 相似文献
The study was designed to quantify cadmium accumulation in different components of sewage treatment ponds during different seasons and to assess risk for human consumption perspective. The study estimated cadmium concentration in water, sludge, Eichhornia, plankton and tilapia fish from anaerobic, facultative, maturation-1 and -4 ponds during pre-monsoon, monsoon and post-monsoon periods. It resulted that cadmium accumulated among different components of anaerobic, facultative, maturation-1 and -4 ponds ranging 0–18, 0–10, 0–7 and 0–15.4 ppb, respectively. During monsoon, highest accumulation was observed in tilapia in both facultative and maturation ponds, but during post-monsoon, plankton community showed highest value in all. The highest bio-magnification of cadmium was recorded during monsoon with varying degrees (facultative pond: 4.39, maturation pond-1: 3.03 and maturation pond-4: 7.08). Cadmium concentration in tilapia lied within WHO’s safe level and may be recommended for human consumption. The concentration of cadmium was estimated by Flame Atomic Absorption Spectrophotometer. The above findings occurred due to chelation, adsorption and sedimentation, absorption and bio-accumulation, ionization, leaching through sediment and rainfall. Water pH (6.7–8.8), dissolved oxygen (0–17 mg L?1), total solids (251–650 mg L?1), iron (Fe2+) concentration (0.61–4.87 mg L?1) and sedimentation rate (278.9–2,409.6 g day?1 m?3) were conducive for the distribution of cadmium into different ecosystem components of treatment ponds. These ponds reclaimed 28.57–61.11 % of sewage-cadmium and may be promoted as a low-cost eco-tech for sewage treatment. 相似文献
