Origin of magnetite in oxidized CV chondrites: in situ measurement of oxygen isotope compositions of Allende magnetite and olivine

Magnetite in the oxidized CV chondrite Allende mainly occurs as spherical nodules in porphyritic-olivine (PO) chondrules, where it is associated with Ni-rich metal and/or sulfides. To help constrain the origin of the magnetite, we measured oxygen isotopic compositions of magnetite and coexisting olivine grains in PO chondrules of Allende by an in situ ion microprobe technique. Five magnetite nodules form a relatively tight cluster in oxygen isotopic composition with delta 18O values from -4.8 to -7.1% and delta 17O values from -2.9 to -6.3%. Seven coexisting olivine grains have oxygen isotopic compositions from -0.9 to -6.3% in delta 18O and from -4.6 to -7.9% in delta 17O. The delta 17O values of the magnetite and coexisting olivine do not overlap; they range from -0.4 to -2.6%, and from -4.0 to -5.7%, respectively. Thus, the magnetite is not in isotopic equilibrium with the olivine in PO chondrules, implying that it formed after the chondrule formation. The delta 17O of the magnetite is somewhat more negative than estimates for the ambient solar nebula gas. We infer that the magnetite formed on the parent asteroid by oxidation of metal by H2O which had previously experienced minor O isotope exchange with fine-grained silicates.     

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

Biomonitoring of coastal pollution status using protozoan communities with a modified PFU method

Structural and functional parameters of protozoan communities were assessed as indicators of water quality in Korean coastal waters in the summer of 2000. A modified polyurethane foam unit (PFU) method, named the bottled PFU (BPFU) system, was used in order to carry out the bioassessment. Both parameters suggested that biomonitoring using the BPFU system was more effective than the conventional PFU method in offshore areas. The species number collected by the BPFU system generally decreased as pollution intensity increased at three main stations and was greater than that collected using the PFU method (paired t-test, t = 4.83, p < 0.0001). The Margalef diversity index coincided well with the water conditions. The diversity index values calculated from the BPFU system were also significantly higher than those from the PFU method (paired t-test, t = 5.37, p < 0.0001). Furthermore, the functional parameters, i.e. S(eq),G and T90%, correlated with the pollution status and could thus clearly discriminate the different classes of water quality.     

The optimal choice of residue management, crop rotations, and cost of carbon sequestration: empirical results in the Midwest US

This study investigates the cost of soil carbon sequestration in the Midwest US. The model addresses several missing components in earlier analyses: the link between the residue level choice and carbon payments, crop rotations, carbon loss when shifting from conservation to conventional tillage and the spatial pattern of carbon sequestration across different soil types. The results suggest that for $100 per metric ton of carbon, 1.5 million metric tons of carbon could be sequestered per year on the 19.9 million hectares of cropland in the study region. These estimates suggest less carbon potential than existing studies because the opportunity costs associated with conservation tillage are fairly high. Annual carbon rental payments are found to be more efficient, as expected, but for smaller programs, per hectare rental payments are not substantially more costly.     

Thermal effects on the growth and fatty acid composition of four harmful algal bloom species: Possible implications for ichthyotoxicity

Little is known regarding how harmful algal bloom species respond to different temperatures in terms of fatty acid production. This study examined the effects of temperature on the growth rates, cell volumes, and fatty acid concentrations and compositions of four harmful algal bloom species (HABs), Akashiwo sanguinea, Alexandrium tamarense, Chattonella ovata, and Prorocentrum minimum. The HABs species were cultured at 15, 20, 25, and 30°C in a nutrient-enriched medium. Three of the species maintained optimal growth rates over a wide range of temperatures, but A. tamarense did not. The cell volumes of each species showed little change over the temperature range. The total fatty acid concentrations in A. sanguinea, A. tamarense and C. ovata decreased as the temperature increased, but P. minimum showed no trend in this respect. Polyunsaturated fatty acids (PUFAs), the key biochemical components that maintain cell membrane fluidity and which are associated with toxicity, decreased in both concentration and proportion of total fatty acids as temperature increased, except in A. sanguinea, in which the proportion of PUFAs to the total fatty acids increased. These reductions in PUFA concentration and proportion could reduce cell membrane fluidity and toxicity in HABs; however, enhanced growth and/or ruptured cells, which are considered more toxic than intact cells, could compensate for the reduced per-cell toxicity. This phenomenon might impact on the marine ecosystem and aquaculture industry.     

Recent increase in surface fCO<Subscript>2</Subscript> in the western subtropical North Pacific

We observed unusually high levels (> 440 μatm) of carbon dioxide fugacity (fCO₂) in surface seawater in the western subtropical North Pacific, the area where Subtropical Mode Water is formed, during summer 2015. The NOAA Kuroshio Extension Observatory moored buoy located in this region also measured high CO₂ values, up to 500 μatm during this period. These high sea surface fCO₂ (fCO_2SW) values are explained by much higher normalized total dissolved inorganic carbon and slightly higher normalized total alkalinity concentrations in this region compared to the equatorial Pacific. Moreover, these values are much higher than the climatological CO₂ values, even considering increasing atmospheric CO₂, indicating a recent large increase in sea surface CO₂ concentrations. A large seasonal change in sea surface temperature contributed to higher surface fCO_2SW in the summer of 2015.     

Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models

Quantitative analyses of groundwater flow and transport typically rely on a physically‐based model, which is inherently subject to error. Errors in model structure, parameter and data lead to both random and systematic error even in the output of a calibrated model. We develop complementary data‐driven models (DDMs) to reduce the predictive error of physically‐based groundwater models. Two machine learning techniques, the instance‐based weighting and support vector regression, are used to build the DDMs. This approach is illustrated using two real‐world case studies of the Republican River Compact Administration model and the Spokane Valley‐Rathdrum Prairie model. The two groundwater models have different hydrogeologic settings, parameterization, and calibration methods. In the first case study, cluster analysis is introduced for data preprocessing to make the DDMs more robust and computationally efficient. The DDMs reduce the root‐mean‐square error (RMSE) of the temporal, spatial, and spatiotemporal prediction of piezometric head of the groundwater model by 82%, 60%, and 48%, respectively. In the second case study, the DDMs reduce the RMSE of the temporal prediction of piezometric head of the groundwater model by 77%. It is further demonstrated that the effectiveness of the DDMs depends on the existence and extent of the structure in the error of the physically‐based model.     

Analysis of recent climate change over the Arctic using ERA-Interim reanalysis data

This study investigates recent climate change over the Arctic and its link to the mid-latitudes using the ERA-Interim global atmospheric reanalysis data from the European Center for Medium-Range Weather Forecast （ECMWF）. Since 1979, sub- stantial surface warming, associated with the increase in anthropogenic greenhouse gases, has occurred over the Arctic. The great- est warming in winter has taken place offshore in the Kara-Barents Sea, and is associated with the increase in turbulent heat fluxes from the marginal ice zone. In contrast to the marked warming over the Arctic Ocean in winter, substantial cooling appears over Siberia and eastern Asia, linked to the reduction of Arctic sea ice during the freezing season （September-March）. However, in summer, very little change is observed in surface air temperature over the Arctic because increased radiative heat melts the sea ice and the amount of turbulent heat gain from the ocean is relatively small. The heat stored in the upper ocean mixed layer in summer with the opening of the Arctic Ocean is released back to the atmosphere as turbulent heat fluxes during the autumn and through to the following spring. This warming of the Arctic and the reduced sea ice amplifies surface cooling over Siberia and eastern Asia in winter.     

Understanding the statistical properties of the percent model affinity index can improve biomonitoring related decision making

The percent model affinity (PMA) index is used to measure the similarity of two probability profiles representing, for example, an ideal profile (i.e. reference condition) and a monitored profile (i.e. possibly impacted condition). The goal of this work is to study the effects of sample size, evenness, true value of the index and number of classes on the statistical properties of the estimator of the PMA index. We derive and extend previous formulas of the expectation and variance of the estimator for estimated monitored profile and fixed reference profile. Using the obtained extension, we find that the estimator is asymptotically unbiased, converging faster when the profiles differ. When both profiles are estimated, we calculate the expectation using transformation rules for expectation and in addition derive the formula for the estimator’s variance. Since the computation of the probabilities in the variance formula is slow, we study the behavior of the variance with simulation experiments and assess whether it could be approximated with the variance for the fixed reference profile. Finally, we provide a set of recommendations for the users of the PMA index to avoid the most common caveats of the index.