The effect of liquid composition on the partitioning of Ni between olivine and silicate melt

We report the results of experiments designed to separate the effects of temperature and pressure from liquid composition on the partitioning of Ni between olivine and liquid, \(D_{\text{Ni}}^{\text{ol/liq}}\). Experiments were performed from 1300 to 1600 °C and 1 atm to 3.0 GPa, using mid-ocean ridge basalt (MORB) glass surrounded by powdered olivine in graphite–Pt double capsules at high pressure and powdered MORB in crucibles fabricated from single crystals of San Carlos olivine at one atmosphere. In these experiments, pressure and temperature were varied in such a way that we produced a series of liquids, each with an approximately constant composition (~12, ~15, and ~21 wt% MgO). Previously, we used a similar approach to show that \(D_{\text{Ni}}^{\text{ol/liq}}\) for a liquid with ~18 wt% MgO is a strong function of temperature. Combining the new data presented here with our previous results allows us to separate the effects of temperature from composition. We fit our data based on a Ni–Mg exchange reaction, which yields \(\ln \left( {D_{\text{Ni}}^{\text{molar}} } \right) = \frac{{ -\Delta _{r(1)} H_{{T_{\text{ref}} ,P_{\text{ref}} }}^{ \circ } }}{RT} + \frac{{\Delta _{r(1)} S_{{T_{\text{ref}} ,P_{\text{ref}} }}^{ \circ } }}{R} - \ln \left( {\frac{{X_{\text{MgO}}^{\text{liq}} }}{{X_{{{\text{MgSi}}_{ 0. 5} {\text{O}}_{ 2} }}^{\text{ol}} }}} \right).\) Each subset of constant composition experiments displays roughly the same temperature dependence of \(D_{\text{Ni}}^{\text{ol/liq}}\) (i.e.,\(-\Delta _{r(1)} H_{{T_{\text{ref}} ,P_{\text{ref}} }}^{ \circ } /R\)) as previously reported for liquids with ~18 wt% MgO. Fitting new data presented here (15 experiments) in conjunction with our 13 previously published experiments (those with ~18 wt% MgO in the silicate liquid) to the above expression gives \(-\Delta _{r(1)} H_{{T_{\text{ref}} ,P_{\text{ref}} }}^{ \circ } /R\) = 3641 ± 396 (K) and \(\Delta _{r(1)} S_{{T_{\text{ref}} ,P_{\text{ref}} }}^{ \circ } /R\) = ? 1.597 ± 0.229. Adding data from the literature yields \(-\Delta _{r(1)} H_{{T_{\text{ref}} ,P_{\text{ref}} }}^{ \circ } /R\) = 4505 ± 196 (K) and \(\Delta _{r(1)} S_{{T_{\text{ref}} ,P_{\text{ref}} }}^{ \circ } /R\) = ? 2.075 ± 0.120, a set of coefficients that leads to a predictive equation for \(D_{\text{Ni}}^{\text{ol/liq}}\) applicable to a wide range of melt compositions. We use the results of our work to model the melting of peridotite beneath lithosphere of varying thickness and show that: (1) a positive correlation between NiO in magnesian olivine phenocrysts and lithospheric thickness is expected given a temperature-dependent \(D_{\text{Ni}}^{\text{ol/liq}} ,\) and (2) the magnitude of the slope for natural samples is consistent with our experimentally determined temperature dependence. Alternative processes to generate the positive correlation between NiO in magnesian olivines and lithospheric thickness, such as the melting of olivine-free pyroxenite, are possible, but they are not required to explain the observed correlation of NiO concentration in initially crystallizing olivine with lithospheric thickness.     

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district,Haryana (India)

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\)), differences of circular vertical and horizontal \(\sigma ^{\mathrm{o}} \, (\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}})\) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (\({\hbox {RMS}}_{\mathrm{height}}\)). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., \(\sigma ^{\mathrm{o}}\). Near surface SM measurements were related to \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\) derived using 5.35 GHz (C-band) image of RISAT-1 and \({\hbox {RMS}}_{\mathrm{height}}\). The roughness component derived in terms of \({\hbox {RMS}}_{\mathrm{height}}\) showed a good positive correlation with \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}} \, (R^{2} = 0.65)\). By considering all the major influencing factors (\(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\)), an SEM was developed where SM (volumetric) predicted values depend on \(\sigma ^{\mathrm{o}}_{\mathrm{RH}}\), \(\sigma ^{\mathrm{o}}_{\mathrm{RV}} {-} \sigma ^{\mathrm{o}}_{\mathrm{RH}}\), and \({\hbox {RMS}}_{\mathrm{height}}\). This SEM showed \(R^{2}\) of 0.87 and adjusted \(R^{2}\) of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (\({\hbox {SM}}_{\mathrm{Observed}}\)) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash–Sutcliffe efficiency (NSE) = 0.91 (\({\approx } 1\)), index of agreement (d) = 1, coefficient of determination \((R^{2}) = 0.87\), mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences \(({\hbox {S}}_{\mathrm{d}}^{2}) = 0.004\). The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on \(\sigma ^{\mathrm{o}}\). By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.     

Strain responses of frozen clay with thermal gradient under triaxial creep

Thermal gradient is one of the main features for the temperature distribution in artificial frozen shaft lining (FSL). The time-dependent strain responses and the corresponding heterogeneity characteristics of frozen soils with thermal gradient are of potential significance for stability assessment and prediction of FSL, especially of the FSL embedded in thick alluvium. A series of triaxial creep tests were carried out on frozen saturated clay under various thermal gradients and creep stresses. The experimental results indicated that the triaxial creep curves for frozen clay with thermal gradient exhibit viscous characteristics, and the creep rate \(\Delta \varepsilon_{\text{a}} /\Delta t\) decreases with the increase in creep time \(t\) and decrease in thermal gradient. The stress–strain curve under different \(t\) showed that the creep stress has a marked growth when axial strain \(\varepsilon_{\text{a}} \le 1\,\%\). However, when \(\varepsilon_{\text{a}} \ge 1\,\%\), the growth rate decreases gradually. The deviation between measured radial strain \(\varepsilon_{\text{r}}^{\text{m}}\) under the middle specimen section height SSH and the calculated radial strain \(\varepsilon_{\text{r}}^{\text{c}}\) from the volumetric strain increases following a unified equation with the increase in axial strain. The radial strain \(\varepsilon_{\text{r}}^{\text{f}}\) for frozen clay with thermal gradient after experiment increases with the increase in SSH, and the slope of \( \varepsilon_{\text{r}}^{\text{f}} - {\text{SSH}} \) curve is significantly dependent on the thermal gradient and creep stress. The variation of \(\varepsilon_{\text{r}}^{\text{m}} - \varepsilon_{\text{r}}^{\text{c }}\) during experiment and \(\varepsilon_{\text{r}}^{\text{f}}\) distribution after experiment are the macro-responses of internal micro-heterogeneities in frozen soils induced from thermal gradient, and are closely related to strain rate and its variation. These observations and findings provide an insight into the creep mechanism and the estimation method of creep deformation for frozen soils with thermal gradient.     

Effect of uncertainty in $$V_{\mathrm{S}}{-}N$$ correlations on seismic site response analysis

This paper studies the reliability of the calculated shear-wave velocity (\(V_{\mathrm{S}}\)) from different available \(V_{\mathrm{S}}\) and SPT-N correlations in terms of seismic site response analysis. In the present study, various \(V_{\mathrm{S}}\)–N correlations developed for different regions around the globe have been used to calculate the bound of \(V_{\mathrm{S}}\) variations with depth at three different sites in Kolkata city. This bound has later been used to generate the random \(V_{\mathrm{S}}\) profiles using the Monte Carlo simulation. Equivalent linear site response analysis has been performed to study the response of those generated profiles under different input motion excitations. Strong-to-weak ground motion records have been used for this purpose. The amplification spectra of the generated \(V_{\mathrm{S}}\) profiles using all soil types and specific soil-type \(V_{\mathrm{S}}\)–N correlations show significant variations. The study also shows that the \(V_{\mathrm{S}}\)–N correlation may result in quite different \(V_{\mathrm{S},30}\) values and subsequently it may lead to the different site classes according to the NEHRP 2003 classification. So, the random choice of the \(V_{\mathrm{S}}\)–N correlation, where the direct measurement of \(V_{\mathrm{S}}\) is not available, may affect the outcome of seismic hazard analysis significantly. The study points out the need for accurate estimation of the \(V_{\mathrm{S}}\) profile either from in-situ determination or using site-specific correlation.     

New paleomagnetic results on $$\sim $$2367 Ma Dharwar giant dyke swarm,Dharwar craton,southern India: implications for Paleoproterozoic continental reconstruction

Here we report new paleomagnetic results and precise paleopole position of the extensional study on \(\sim \)2367 Ma mafic giant radiating dyke swarm in the Dharwar craton, southern India. We have sampled 29 sites on 12 dykes from NE–SW Karimnagar–Hyderabad dykes and Dhone–Gooty sector dykes, eastern Dharwar craton to provide unambiguous paleomagnetism evidence on the spectacular radiating dyke swarm and thereby strengthening the presence of single magmatic event at \(\sim \)2367 Ma. A total of 158 samples were subjected to detailed alternating field and thermal demagnetization techniques and the results are presented here along with previously reported data on the same dyke swarm. The remanent magnetic directions are showing two components, viz., seven sites representing four dykes show component (A) with mean declination of \(94{{}^{\circ }}\) and mean inclination of \(-\,70{{}^{\circ }}\) (\(\hbox {k}=87\), \(\upalpha _{95}=10{{}^{\circ }}\)) and corresponding paleopole at \(16{{}^{\circ }}\hbox {N}\), \(41{{}^{\circ }}\hbox {E}\) (\(\hbox {dp}=15{{}^{\circ }}\) and \(\hbox {dm}=17{{}^{\circ }}\)) and 22 sites representing 8 dykes yielded a component (B) with mean declination of \(41{{}^{\circ }}\) and mean inclination of \(-\,21{{}^{\circ }}\) (\(\hbox {k}=41\), \(\upalpha _{95}=9{{}^{\circ }}\)) with a paleopole at \(41{{}^{\circ }}\hbox {N}\), \(200{{}^{\circ }}\hbox {E}\) (\(\hbox {dp}=5{{}^{\circ }}\) and \(\hbox {dm}=10{{}^{\circ }}\)). Component (A) results are similar to the previously reported directions from the \(\sim \)2367 Ma dyke swarm, which have been confirmed fairly reliably to be of primary origin. The component (B) directions appear to be strongly overprinted by the 2080 Ma event. The grand mean for the primary component (A) combined with earlier reported studies gives mean declination of \(97{{}^{\circ }}\) and mean inclination of \(-\,79{{}^{\circ }}\) (\(\hbox {k}=55\), \(\upalpha _{95}=3{{}^{\circ }}\)) with a paleopole at \(15{{}^{\circ }}\hbox {N}\), \(57{{}^{\circ }}\hbox {E}\) (\(\hbox {dp}=5{{}^{\circ }}\), \(\hbox {dm}=6{{}^{\circ }}\)). Paleogeographical position for the Dharwar craton at \(\sim \)2367 Ma suggests that there may be a chance to possible spatial link between Dharwar dykes of Dharwar craton (India), Widgemooltha and Erayinia dykes of Yilgarn craton (Australia), Sebanga Poort Dykes of Zimbabwe craton (Africa) and Karelian dykes of Kola-Karelia craton (Baltica Shield).     

Fluvial geochemistry of Subarnarekha River basin,India

The fluvial geochemistry of the Subarnarekha River and its major tributaries has been studied on a seasonal basis in order to assess the geochemical processes that explain the water composition and estimate solute fluxes. The analytical results show the mildly acidic to alkaline nature of the Subarnarekha River water and the dominance of \(\hbox {Ca}^{2+}\) and \(\hbox {Na}^{+}\) in cationic and \(\hbox {HCO}_{3}^{-}\) and \({\hbox {Cl}}^{-}\) in anionic composition. Minimum ionic concentration during the monsoon and maximum concentration in the pre-monsoon seasons reflect concentrating effects due to decrease in the river discharge and increase in the base flow contribution during the pre-monsoon and dilution effects of atmospheric precipitation in the monsoon season. The solute acquisition processes are mainly controlled by weathering of rocks, with minor contribution from marine and anthropogenic sources. Higher contribution of alkaline earth \((\hbox {Ca}^{2+}{+}\,\hbox {Mg}^{2+})\) to the total cations \((\hbox {TZ}^{+})\) and high \((\hbox {Na}^{+}+\hbox {K}^{+})/\hbox {Cl}^{-}\), \((\hbox {Na}^{+}+\hbox {K}^{+})/\hbox {TZ}^{+}\), \(\hbox {HCO}_{3}^{-}/(\hbox {SO}_{4}^{2-}+\hbox {Cl}^{-})\) and low \((\hbox {Ca}^{2+}+\hbox {Mg}^{2+})/(\hbox {Na}^{+}+\hbox {K}^{+})\) equivalent ratios suggest that the Subarnarekha River water is under the combined influence of carbonate and silicate weathering. The river water is undersaturated with respect to dolomite and calcite during the post-monsoon and monsoon seasons and oversaturated in the pre-monsoon season. The pH–log \(\hbox {H}_{4}\hbox {SiO}_{4}\) stability diagram demonstrates that the water chemistry is in equilibrium with the kaolinite. The Subarnarekha River annually delivered \(1.477\times 10^{6}\) ton of dissolved loads to the Bay of Bengal, with an estimated chemical denudation rate of \(77\hbox { ton km}^{-2}\hbox { yr}^{-1}\). Sodium adsorption ratio, residual sodium carbonate and per cent sodium values placed the studied river water in the ‘excellent to good quality’ category and it can be safely used for irrigation.     

Pressuremeter Modulus and Limit Pressure of Clayey Soils Using <Emphasis Type="Italic">GMDH</Emphasis>-Type Neural Network and Genetic Algorithms

Pressuremeter modulus (\(E_{M}\)) and limit pressure (\(P_{L}\)) are used for the calculation of the settlement and bearing capacity of foundation respectively. As the determination of these parameters from pressuremeter test (PMT) is relatively time-consuming and expensive, various empirical correlations have been proposed to correlate the \(E_{M}\) and \(P_{L}\) to other soil parameters. For the existing equations are incapable of estimating these PMT parameters well, in present research group method of data handling type neural network is used to estimate the \(E_{M}\) and \(P_{L}\) of clayey soils. The \(E_{M}\) and \(P_{L}\) were modeled as a function of three variables including the moisture content (\(\omega\)), plasticity index and corrected SPT blow counts (\(N_{60}\)). A database containing 51 data sets have been used for training and testing of the models. The performances of proposed models are compared with those of existing empirical equations. The results demonstrate that appreciable improvement with respect to the other correlations has been achieved. At the end, sensitivity analysis of the obtained models has been performed to study the influence of input parameters on model outputs and shows that the \(N_{60}\) is the most influential parameter on the PMT parameters.     

Application of environmental isotopes and hydrochemistry in the identification of source of seepage and likely connection with lake water in Lesser Himalaya,Uttarakhand, India

Oxygen (\({\updelta }^{18}\hbox {O}\)) and hydrogen (\({\updelta }^{2}\hbox {H}\) and \(^{3}\hbox {H}\)) isotopes of water, along with their hydrochemistry, were used to identify the source of a newly emerged seepage water in the downstream of Lake Nainital, located in the Lesser Himalayan region of Uttarakhand, India. A total of 57 samples of water from 19 different sites, in and around the seepage site, were collected. Samples were analysed for chemical tracers like \(\hbox {Ca}^{++}\), \(\hbox {Mg}^{++}\), \(\hbox {Na}^{+}\), \(\hbox {K}^{+}\), \({\hbox {SO}_{4}}^{--}\) and \(\hbox {Cl}^{-}\) using an Ion Chromatograph (Dionex IC-5000). A Dual Inlet Isotope Ratio Mass Spectrometer (DIIRMS) and an Ultra-Low Level Liquid Scintillation Counter (ULLSC), were used in measurements of stable isotopes (\({\updelta }^{2}\hbox {H}\) and \({\updelta }^{18}\hbox {O}\)) and a radioisotope (\(^{3}\hbox {H}\)), respectively. Results obtained in this study repudiate the possibility of any likely connection between seepage water and the lake water, and indicate that the source of seepage water is mainly due to locally recharged groundwater. The study suggests that environmental isotopes (\({\updelta }^{2}\hbox {H}\), \({\updelta }^{18}\hbox {O}\) and \(^{3}\hbox {H}\)) can effectively be used as ‘tracers’ in the detection of the source of seepage water in conjunction with other hydrochemical tracers, and can help in water resource management and planning.     

Competing effects of crystal chemistry and silicate melt composition on trace element behavior in magmatic systems: insights from crystal/silicate melt partitioning of the REE,HFSE, Sn,In, Ga,Ba, Pt and Rh

We present new partition coefficients for the REE, HFSE, Sn, In, Ga, Ba, Pt and Rh between clinopyroxene, olivine and basaltic melt as a function of crystal chemistry and melt composition at temperatures of 1190–1300 °C and 1-bar pressure. Two components, namely \(\mathrm {Al_2O_3}\) and \(\mathrm {Na_2O}\), were chosen to be investigated since they are known to affect the structure of silicate melts and especially clinopyroxene crystal chemistry. The amount of \(^{[4]}\mathrm{Al}\) in clinopyroxene will result in an increase of \(D_i^\mathrm{{cpx/melt}}\) even after applying a correction factor to account for the effect of melt polymerization. Moreover, the positive correlation between \(^{[4]}\mathrm{Al}\) and \(D_i^\mathrm{{cpx/melt}}\) is not restricted to the REE, but also applies for Sn, Ga, In, and Ba. The addition of up to 2.6 wt% \(\mathrm {Na_2O}\) to the silicate melt universally increases the \(D_i^\mathrm{{cpx/melt}}\) without any concomitant change in crystal chemistry or a significant effect in melt polymerization. This compositional effect is likely due to the ability of Na to break REE–Al complexes in the melt. Our results emphasize the importance of considering all variables that affect the behavior of trace elements in magmatic systems before applying the lattice strain model and derive meaningful results for the changes in the parameters of the crystallographic sites.     

The high-pressure stability of chlorite and other hydrates in subduction mélanges: experiments in the system Cr<Subscript>2</Subscript>O<Subscript>3</Subscript>–MgO–Al<Subscript>2</Subscript>O<Subscript>3</Subscript>–SiO<Subscript>2</Subscript>–H<Subscript>2</Subscript>O

The solubility of chromium in chlorite as a function of pressure, temperature, and bulk composition was investigated in the system Cr₂O₃–MgO–Al₂O₃–SiO₂–H₂O, and its effect on phase relations evaluated. Three different compositions with X _Cr = Cr/(Cr + Al) = 0.075, 0.25, and 0.5 respectively, were investigated at 1.5–6.5 GPa, 650–900 °C. Cr-chlorite only occurs in the bulk composition with X _Cr = 0.075; otherwise, spinel and garnet are the major aluminous phases. In the experiments, Cr-chlorite coexists with enstatite up to 3.5 GPa, 800–850 °C, and with forsterite, pyrope, and spinel at higher pressure. At P > 5 GPa other hydrates occur: a Cr-bearing phase-HAPY (Mg_2.2Al_1.5Cr_0.1Si_1.1O₆(OH)₂) is stable in assemblage with pyrope, forsterite, and spinel; Mg-sursassite coexists at 6.0 GPa, 650 °C with forsterite and spinel and a new Cr-bearing phase, named 11.5 Å phase (Mg:Al:Si = 6.3:1.2:2.4) after the first diffraction peak observed in high-resolution X-ray diffraction pattern. Cr affects the stability of chlorite by shifting its breakdown reactions toward higher temperature, but Cr solubility at high pressure is reduced compared with the solubility observed in low-pressure occurrences in hydrothermal environments. Chromium partitions generally according to \(X_{\text{Cr}}^{\text{spinel}}\) ? \(X_{\text{Cr}}^{\text{opx}}\) > \(X_{\text{Cr}}^{\text{chlorite}}\) ≥ \(X_{\text{Cr}}^{\text{HAPY}}\) > \(X_{\text{Cr}}^{\text{garnet}}\). At 5 GPa, 750 °C (bulk with X _Cr = 0.075) equilibrium values are \(X_{\text{Cr}}^{\text{spinel}}\) = 0.27, \(X_{\text{Cr}}^{\text{chlorite}}\) = 0.08, \(X_{\text{Cr}}^{\text{garnet}}\) = 0.05; at 5.4 GPa, 720 °C \(X_{\text{Cr}}^{\text{spinel}}\) = 0.33, \(X_{\text{Cr}}^{\text{HAPY}}\) = 0.06, and \(X_{\text{Cr}}^{\text{garnet}}\) = 0.04; and at 3.5 GPa, 850 °C \(X_{\text{Cr}}^{\text{opx}}\) = 0.12 and \(X_{\text{Cr}}^{\text{chlorite}}\) = 0.07. Results on Cr–Al partitioning between spinel and garnet suggest that at low temperature the spinel- to garnet-peridotite transition has a negative slope of 0.5 GPa/100 °C. The formation of phase-HAPY, in assemblage with garnet and spinel, at pressures above chlorite breakdown, provides a viable mechanism to promote H₂O transport in metasomatized ultramafic mélanges of subduction channels.     

Influence of ignimbrite on the chemistry of river water in Shirasu plateau,Japan

River, rain and spring water samples from a region covered in “Shirasu” ignimbrite were collected on Kyushu Island, Japan. The analytical results were subjected to multivariate statistical analysis and stoichiometric calculation to understand the geographical distribution of chemical components in water and to extract geochemical underlying factors. The multivariate statistical analysis showed that the river-water chemistry is only slightly influenced by hot springs or polluted waters, but is highly controlled by weathering of ignimbrite. On the basis of the stoichiometric calculation based on water–rock interaction, the water chemistry was successfully estimated by a simple equation:\({\left[ {{\text{Si}}} \right]}{\text{ = 2}}{\left[ {{\text{Na}}^{{\text{ + }}} } \right]}{\text{ + }}{\left[ {{\text{Mg}}^{{{\text{2 + }}}} } \right]}\) in the upstream area, complemented by \({\left[ {{\text{Si}}} \right]}{\text{ = }}{\left[ {{\text{Na}}^{{\text{ + }}} } \right]}{\text{ - 3}}{\left[ {{\text{K}}^{{\text{ + }}} } \right]}{\text{ + }}{\left[ {{\text{Mg}}^{{{\text{2 + }}}} } \right]}{\text{ - 2}}{\left[ {{\text{Ca}}^{{{\text{2 + }}}} } \right]}\) in the downstream area.     

Characteristics of surface ozone in Agra,a sub-urban site in Indo-Gangetic Plain

In the present study, measurements of surface ozone (\(\hbox {O}_{3}\)) and its precursors (NO and \(\hbox {NO}_{2}\)) were carried out at a sub-urban site of Agra (\(27{^{\circ }}10'\hbox {N}\), \(78{^{\circ }}05'\hbox {E}\)), India during May 2012–May 2013. During the study period, average concentrations of \(\hbox {O}_{3}\), NO, and \(\hbox {NO}_{2}\) were \(39.6 \pm 25.3\), \(0.8 \pm 0.8\) and \(9.1 \pm 6.6 \, \hbox {ppb}\), respectively. \(\hbox {O}_{3}\) showed distinct seasonal variation in peak value of diurnal variation: summer \({>}\) post-monsoon \({>}\) winter \({>}\) monsoon. However, \(\hbox {NO}_{2}\) showed highest levels in winter and lowest in monsoon. The average positive rate of change of \(\hbox {O}_{3}\) (08:00–11:00 hr) was highest in April (16.3 ppb/hr) and lowest in August (1.1 ppb/hr), while average negative rate of change of \(\hbox {O}_{3}\) (17:00–19:00 hr) was highest in December (–13.2 ppb/hr) and lowest in July (–1.1 ppb/hr). An attempt was made to identify the \(\hbox {VOC--NO}_{\mathrm{x}}\) sensitivity of the site using \(\hbox {O}_{3}/\hbox {HNO}_{3}\) ratio as photochemical indicator. Most of the days this ratio was above the threshold value (12–16), which suggests \(\hbox {NO}_{\mathrm{x}}\) sensitivity of the site. The episodic event of ozone was characterized through meteorological parameters and precursors concentration. Fine particles (\(\hbox {PM}_{2.5}\)) cause loss of ozone through heterogeneous reactions on their surface and reduction in solar radiation. In the study, statistical analyses were used to estimate the amount of ozone loss.     

Ammonium Silicate Stability Relations

A method has been developed to control ammonium fugacity, \(f_{{\text{NH}}_{3}}\), at elevated temperatures and pressures. The method uses an internal nitrogen buffer, the assemblage Cr + CrN, in conjunction with a traditional external hydrogen buffer. In this manner, all gas fugacities in the system N-O-H can be calculated.The Cr + CrN buffer has been applied to study equilibria between buddingtonite (ammonium feldspar), ammonium muscovite, sillimanite, and quartz at a constant gas pressure of 2,000 bars. Two of the five relevant reactions were measured experimentally; from these data, it is possible to calculate isothermal sections at 500, 600, and 700° C.Below 600° C, ammonium muscovite is stable even at extremely low levels of \(f_{{\text{NH}}_{3}}\), while buddingtonite requires \(f_{{\text{NH}}_{3}}\;\geqq\;10^4\) bars. Release of NH₃ during progressive metamorphism can be achieved by three processes: thermal decomposition, dehydration, and cation exchange. Within the crust, \(f_{{\text{NH}}_{3}}\) predominates over \(f_{{\text{N}}_{2}}\) by several orders of magnitude; but on the surface, nitrogen released as NH₃ by metamorphism will be oxidized to N₂. Biological materials provide important intermediate storage for nitrogen compounds during the nitrogen cycle.     

Wet season hydrochemistry of Bribin Cave in Gunung Sewu Karst, Indonesia

This research was conducted on the Bribin River, the most important underground river in the Gunung Sewu Karst, Gunung Kidul, Java, Indonesia. The main purpose of this study was to define the wet-season hydrochemistry of this river. This research also focuses on identifying the relationship between hydrochemical parameters to provide better aquifer characterization. Water-level monitoring and discharge measurements were conducted over a 1-year period to define the discharge hydrograph. Furthermore, baseflow-separation analysis is conducted to determine the diffuse-flow percentage throughout the year. Water sampling for hydrogeochemical analysis is taken every month in the wet season and every 2?hours for two selected flood events. To describe the hydrogeochemical processes, a bivariate plot analysis of certain hydrochemical parameters is conducted. The results show that the diffuse-flow percentage significantly controls the river hydrochemistry. The domination of diffuse flow occurs during non-flooding and flood recession periods, which are typified by a high value of calcium and bicarbonate and low CO₂ gas content in water. Conversely, the hydrochemistry of flood events is characterized by the domination of conduit flow and CO₂ gas with low calcium and bicarbonate content. According to the wet-season hydrochemistry, it seems that the small- and medium-sized fractures in the Bribin aquifer still provide storage for the diffuse and fissure flows, although the conduit fracture is already developed.     

Data-driven nonlinear modeling studies on removal of Acid Yellow 59 using Si-doped multi-walled carbon nanotubes

Data-driven modeling of removal of color index name of Acid Yellow 59 from aqueous solutions using multi-walled carbon nanotubes by multiple (non)linear regression and artificial neural networks (ANN) models based on leave-one-out cross-validation to predict the adsorbed dye amount per unit mass of adsorbent (mg g^?1) and performance evaluation of the proposed multiple (non)linear regression and ANN models is the main novel contributor of the present study. Initial dye concentration, adsorbent concentration, reaction time, and temperature were determined as explanatory variables and input neurons for multiple (non)linear regression and ANN models, respectively. The total number of experiments was determined as 1280 statistically. The results showed that multilayer perception ANN model (\(R^{2}_{\text{training}}\) = 0.9997, \(R^{2}_{\text{testing}}\) = 0.9993, RMSE = 0.7678, MAE of 0.0007) predicted q _t better than multiple (non)linear regression model (\(R^{2}_{\text{adj}}\) = 0.9645, \(R^{2}_{\text{pred}}\) = 0.9633, SE = 9.55) and MLR (R ² = 0.9543, SE = 10.87) models. The results justified the accuracy of ANN in prediction of q _t , significantly.     

Si and O self-diffusion in hydrous forsterite and iron-bearing olivine from the perspective of defect chemistry

We discuss the experimental results of silicon and oxygen self-diffusion coefficients in forsterite and iron-bearing olivine from the perspective of defect chemistry. Silicon diffusion is dominated by V_O ^··-associated V_Si″″, whereas oxygen diffusion is dominated by hopping of V_O ^·· under anhydrous conditions, and by (OH)_O ^· under hydrous conditions. By considering the charge neutrality condition of [(OH)_O ^·] = 2[V_Me″] in hydrous forsterite and iron-bearing olivine, we get D _Si ∝ (\(C_{{{\text{H}}_{2} {\text{O}}}}\))^1/3 and D _O ∝ (\(C_{{{\text{H}}_{2} {\text{O}}}}\))⁰, which explains the experimental results of water effects on oxygen and silicon self-diffusion rates (Fei et al. in Nature 498:213–215, 2013; J Geophys Res 119:7598–7606, 2014). The \(C_{{{\text{H}}_{2} {\text{O}}}}\) dependence of creep rate in the Earth’s mantle should be close to that given by Si and O self-diffusion coefficients obtained under water unsaturated conditions.     

Trend analysis of evapotranspiration over India: Observed from long-term satellite measurements

Owing to the lack of consistent spatial time series data on actual evapotranspiration (ET), very few studies have been conducted on the long-term trend and variability in ET at a national scale over the Indian subcontinent. The present study uses biome specific ET data derived from NOAA satellite’s advanced very high resolution radiometer to investigate the trends and variability in ET over India from 1983 to 2006. Trend analysis using the non-parametric Mann–Kendall test showed that the domain average ET decreased during the period at a rate of \(0.22\,\hbox {mm year}^{-1}\). A strong decreasing trend (\(m = -1.75\, \hbox {mm year}^{-1}\), \(F = 17.41\), \(P\) 0.01) was observed in forest regions. Seasonal analyses indicated a decreasing trend during southwest summer monsoon (\(m= -0.320\, \hbox {mm season}^{-1}\,\hbox {year}^{-1})\) and post-monsoon period (\(m= -0.188\, \hbox {mm season}^{-1 }\,\hbox {year}^{-1})\). In contrast, an increasing trend was observed during northeast winter monsoon (\(m = 0.156 \,\hbox {mm season}^{-1 }\,\hbox {year}^{-1})\) and pre-monsoon (\(m = 0.068\, \hbox {mm season}^{-1 }\,\hbox {year}^{-1})\) periods. Despite an overall net decline in the country, a considerable increase ( \(4 \,\hbox {mm year}^{-1}\)) was observed over arid and semi-arid regions. Grid level correlation with various climatic parameters exhibited a strong positive correlation (\(r \!>\!0.5\)) of ET with soil moisture and precipitation over semi-arid and arid regions, whereas a negative correlation (\(r\) \(-0.5\)) occurred with temperature and insolation in dry regions of western India. The results of this analysis are useful for understanding regional ET dynamics and its relationship with various climatic parameters over India. Future studies on the effects of ET changes on the hydrological cycle, carbon cycle, and energy partitioning are needed to account for the feedbacks to the climate.     

A numerical model in predicting the initial karst development in porous limestone

The numerical model proposed in this paper represents a new method in predicting initial karst spatial distribution of carbonate aquifers. Karstification Index, \( \varPhi_{\text{K}} \), is a measure of the possible largest amount of calcite that can be dissolved from a unit area of rock and transported by groundwater flow during a unit length of time. Mathematically, it is defined as \( \varPhi_{\text{K}} = q \cdot s \), where q is the flux of groundwater and s is the rock solubility; and Karstification Index, \( \varPhi_{\text{K}} \), is the function of flow, heat, and calcium concentration. The numerical model simulates the time- and space-dependent susceptibility to, and process of, karstification by iterative calculations between \( \varPhi_{\text{K}} \) and hydraulic conductivity. A sample calculation is presented on a hypothetical limestone plateau with steep escarpment, a spring, and a river on one side, a vertical no flow boundary on the other side, and a horizontal impermeable base. The rock has an equivalent porous-medium hydraulic conductivity of K?=?5E?8 ms^?1, which is characteristic to non-karstified limestone. Initially, the spring is located at the intersection of the water table and the escarpment’s face some distance above the river. The simulation result showed that the water table is lowered in 2325 years across the plateau, but the spring’s drop is stopped after 65 years. Karstification is most intensive along the water table due to the higher value of Karstification Index which resulted from the intensive flux of the water. The continual decline of the water table results in a laterally elongated and progressively expanding body of karstified rock and finally as a horizontal conduit at river level. The concentration of Ca²⁺ and conduit diameter decrease from the spring toward the impermeable vertical wall on the opposite side and downward to the base of the plateau. The results are consistent with general theories and empirical observations of karst and cave evolution.     

Ocean sea-ice modelling in the Southern Ocean around Indian Antarctic stations

An eddy-resolving coupled ocean sea-ice modelling is carried out in the Southern Ocean region (9\(^{\circ }\)–78\(^{\circ }\)E; 51\(^{\circ }\)–71\(^{\circ }\)S) using the MITgcm. The model domain incorporates the Indian Antarctic stations, Maitri (11.7\({^{\circ }}\)E; 70.7\({^{\circ }}\)S) and Bharati (76.1\({^{\circ }}\)E; 69.4\({^{\circ }}\)S). The realistic simulation of the surface variables, namely, sea surface temperature (SST), sea surface salinity (SSS), surface currents, sea ice concentration (SIC) and sea ice thickness (SIT) is presented for the period of 1997–2012. The horizontal resolution of the model varies between 6 and 10 km. The highest vertical resolution of 5 m is taken near the surface, which gradually increases with increasing depths. The seasonal variability of the SST, SSS, SIC and currents is compared with the available observations in the region of study. It is found that the SIC of the model domain is increasing at a rate of 0.09% per month (nearly 1% per year), whereas, the SIC near Maitri and Bharati regions is increasing at a rate of 0.14 and 0.03% per month, respectively. The variability of the drift of the sea-ice is also estimated over the period of simulation. It is also found that the sea ice volume of the region increases at the rate of 0.0004 \(\hbox {km}^{3}\) per month (nearly 0.005 \(\hbox {km}^{3}\) per year). Further, it is revealed that the accumulation of sea ice around Bharati station is more as compared to Maitri station.