Relationship between sand-dust weather and water dynamics of desert areas in the middle reaches of Heihe River

Sand-dust weather has become an international social-environmental issue of common concern, and constitutes a serious threat to human lives and economic development. In order to explore the responses of natural desert sand and dust to the dynamics of water in desertification, we extracted long-term monitoring data related to precipitation, soil water, groundwater, and sand-dust weather. These data originated from the test stations for desertification control in desert areas of the middle reaches of the Heihe River. We used an algorithm of characteristic parameters, correlations, and multiple regression analysis to establish a regression model for the duration of sand-dust weather. The response char-acteristics of the natural desert sand and dust and changes of the water inter-annual and annual variance were also examined. Our results showed： （1） From 2006 to 2014 the frequency, duration, and volatility trends of sand-dust weather obviously increased, but the change amplitudes of precipitation, soil water, and groundwater level grew smaller. （2） In the vegetative growth seasons from March to November, the annual variance rates of the soil moisture content in each of four studied layers of soil samples were similar, and the changes in the frequency and duration of sand-dust weather were similar. （3） Our new regression equation for the duration of sand-dust weather passed the R test, F test, and t test. By this regression model we could predict the duration of sand-dust weather with an accuracy of 42.9%. This study can thus provide technological support and reference data for water resource management and re-search regarding sand-dust weather mechanisms.

Impact of mica on geotechnical behavior of weathered granitic soil using macro and micro investigations

The micaceous weathered granitic soil(WGS)is frequently encountered in civil engineering worldwide,unfortunately little information is available regarding how mica affects the physico-mechanical behaviors of WGS.This study prepares reconstituted WGS with different mica contents by removing natural mica in theWGS,and then mixes it with commercial mica powders.The geotechnical behavior as well as the microstructures of the mixtures are characterized.The addition of mica enables the physical indices of WGS to be specific combinations of coarser gradation and high permeability but high Atterberg limits.However,high mica content in WGS was found to be associated with undesirable mechanical properties,including increased compressibility,disintegration,and swelling potential,as well as poor compactability and low effective frictional angle.Microstructural analysis indicates that the influence of mica on the responses of mixtures originates from the intrinsic nature of mica as well as the particle packing being formed withinWGS.Mica exists in the mixture as stacks of plates that form a spongy structure with high compressibility and swelling potential.Pores among the plates give the soil high water retention and high Atterberg limits.Large pores are also generated by soil particles with bridging packing,which enhances the permeability and water-soil interactions upon immersion.This study provides a microlevel understanding of how mica dominates the behavior of WGS and provides new insights into the effective stabilization and improvement of micaceous soils.

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,under-dispersed.As such,forecast calibration tools have become popular.Among those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive performance.Nevertheless,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated forecasts.On this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing.The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive entries.The empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models.Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all competitors.Furthermore,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

Promising Results Predict Role for Artificial Intelligence in Weather Forecasting

Artificial intelligence(AI)has already demonstrated its proficiency at difficult scientific tasks like predicting how proteins will fold and identifying new astronomical objects in masses of observational data[1].Now,recent results suggest that AI also excels at weather forecasting.For global predictions,GraphCast,an AI system developed by Google subsidiary DeepMind(London,UK),outperforms the state-of-the-art model from the European Centre for Medium-Range Weather Forecasts(ECMWF),providing more accurate projections of variables such as temperature and humidity 90%of the time[2,3].Other AI systems,including Pangu-Weather from the Chinese tech company Huawei(Shenzhen,China)[4],can also match or beat traditional global forecasting models.

Evaluation of a Wireless Solar Powered Personal Weather Station

We are evaluating dryland cotton production in Martin County, Texas, measuring cotton lint yield per unit of rainfall. Our goal is to collect rainfall data per 250 - 400 ha. Upon selection of a rainfall gauge, we realized that the cost of using, for example, a tipping bucket-type rain gauge would be too expensive and thus searched for an alternative method. We selected an all-in-one commercially available weather station;hereafter, referred to as a Personal Weather Station (PWS) that is both wireless and solar powered. Our objective was to evaluate average measurements of rainfall obtained with the PWS and to compare these to measurements obtained with an automatic weather station (AWS). For this purpose, we installed four PWS deployed within 20 m of the Plant Stress and Water Conservation Meteorological Tower that was used as our AWS, located at USDA-ARS Cropping Systems Research Laboratory, Lubbock, TX. In addition, we measured and compared hourly average values of short-wave irradiance (Rg), air temperature (Tair) and relative humidity (RH), and wind speed (WS), and calculated values of dewpoint temperature (Tdew). This comparison was done over a 242-day period (1 October 2022-31 May 2023) and results indicated that there was no statistical difference in measurements of rainfall between the PWS and AWS. Hourly average values of Rg measured with the PWS and AWS agreed on clear days, but PWS measurements were higher on cloudy days. There was no statistical difference between PWS and AWS hourly average measurements of Tair, RH, and calculated Tdew. Hourly average measurements of Rg and WS were more variable. We concluded that the PWS we selected will provide adequate values of rainfall and other weather variables to meet our goal of evaluating dryland cotton lint yield per unit rainfall.

Effects of weathering depth and thickness on rock failure:Experimental approach and particle flow code simulation

This study investigated the effects of weathering depth and thickness on the failure mechanisms of rock samples through experimental and numerical methods.The first configuration involved conducting artificial weathering on limestone using the freezing and thawing(F-T)for 40 cycles.The mechanical parameters of the samples were measured at the end of the 40th cycle.In the second configuration,a series of specimens underwent salt crystallization(S-C)tests for 20 cycles.Experimental results were validated using discrete element method(DEM).Next,the weathered limestone model with dimensions of 108 mm54 mm were prepared.The weathering layers were tested at four different thicknesses(i.e.2.5 mm,5 mm,7.5 mm,and 10 mm)and three different positions(at the surface,5 mm under the rock surface,and 10 mm under the rock surface).According to the results,weathering depth and thickness have a considerable effect on the failure process.The results also showed a correlation between the values of compressive strength and failure mechanisms associated with the weathering layer.The numerical results revealed that the tension crack was the dominant factor.Additionally,with increasing weathering thickness,Young's modulus,crack initiation stress,and final strength decreased in constant weathering depth.The results also demonstrated that the failure progress of the numerical models was similar to that observed in the laboratory.

Photovoltaic Power Generation Power Prediction under Major Extreme Weather Based on VMD-KELM

The output of photovoltaic power stations is significantly affected by environmental factors,leading to intermittent and fluctuating power generation.With the increasing frequency of extreme weather events due to global warming,photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions.The integration of these stations on a large scale into the power grid could potentially pose challenges to systemstability.To address this issue,in this study,we propose a network architecture based on VMDKELMfor predicting the power output of photovoltaic power plants during severe weather events.Initially,a grey relational analysis is conducted to identify key environmental factors influencing photovoltaic power generation.Subsequently,GMM clustering is utilized to classify meteorological data points based on their probabilities within different Gaussian distributions,enabling comprehensive meteorological clustering and extraction of significant extreme weather data.The data are decomposed using VMD to Fourier transform,followed by smoothing processing and signal reconstruction using KELM to forecast photovoltaic power output under major extreme weather conditions.The proposed prediction scheme is validated by establishing three prediction models,and the predicted photovoltaic output under four major extreme weather conditions is analyzed to assess the impact of severe weather on photovoltaic power station output.The experimental results show that the photovoltaic power output under conditions of dust storms,thunderstorms,solid hail precipitation,and snowstorms is reduced by 68.84%,42.70%,61.86%,and 49.92%,respectively,compared to that under clear day conditions.The photovoltaic power prediction accuracies,in descending order,are dust storms,solid hail precipitation,thunderstorms,and snowstorms.

Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars

With the development of the hyperspectral remote sensing technique,extensive chemical weathering profiles have been identified on Mars.These weathering sequences,formed through precipitation-driven leaching processes,can reflect the paleoenvironments and paleoclimates during pedogenic processes.The specific composition and stratigraphic profiles mirror the mineralogical and chemical trends observed in weathered basalts on Hainan Island in south China.In this study,we investigated the laboratory reflectance spectra of a 53-m-long drilling core of a thick basaltic weathering profile collected from Hainan Island.We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression(GA-PLSR)to predict the chemical properties(SiO2,Al2O3,Fe2O3)and index of laterization(IOL).The entire sample set was divided into a calibration set of 25 samples and a validation set of 12 samples.Specifically,the GA was used to select the spectral subsets for each composition,which were then input into the PLSR model to derive the chemical concentration.The coefficient of determination(R2)values on the validation set for SiO2,Al2O3,Fe2O3,and the IOL were greater than 0.9.In addition,the effects of various spectral preprocessing techniques on the model accuracy were evaluated.We found that the spectral derivative treatment boosted the prediction accuracy of the GA-PLSR model.The improvement achieved with the second derivative was more pronounced than when using the first derivative.The quantitative model developed in this work has the potential to estimate the contents of similar weathering basalt products,and thus infer the degree of alteration and provide insights into paleoclimatic conditions.Moreover,the informative bands selected by the GA can serve as a guideline for designing spectral channels for the next generation of spectrometers.

Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model

Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural language generation methods based on the sequence-to-sequence model,space weather forecast texts can be automatically generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural network sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generating tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecasters in generating high-quality space weather forecast products,despite the data being starved.

2023: Weather and Climate Extremes Hitting the Globe with Emerging Features

Globally,2023 was the warmest observed year on record since at least 1850 and,according to proxy evidence,possibly of the past 100000 years.As in recent years,the record warmth has again been accompanied with yet more extreme weather and climate events throughout the world.Here,we provide an overview of those of 2023,with details and key background causes to help build upon our understanding of the roles of internal climate variability and anthropogenic climate change.We also highlight emerging features associated with some of these extreme events.Hot extremes are occurring earlier in the year,and increasingly simultaneously in differing parts of the world(e.g.,the concurrent hot extremes in the Northern Hemisphere in July 2023).Intense cyclones are exacerbating precipitation extremes(e.g.,the North China flooding in July and the Libya flooding in September).Droughts in some regions(e.g.,California and the Horn of Africa)have transitioned into flood conditions.Climate extremes also show increasing interactions with ecosystems via wildfires(e.g.,those in Hawaii in August and in Canada from spring to autumn 2023)and sandstorms(e.g.,those in Mongolia in April 2023).Finally,we also consider the challenges to research that these emerging characteristics present for the strategy and practice of adaptation.

Weathering and accumulation of trace elements in the soils of the Porali Plain, Balochistan: repercussions in agriculture

This study is thefirst attempt to assess the nature of the soil,especially on the western side of the Porali Plain in Balochistan;a new emerging agriculture hub,using weathering and pollution indices supplemented by multi-variate analysis based on geochemical data.The outcomes of this study are expected to help farmers in soil manage-ment and selecting suitable crops for the region.Twenty-five soil samples were collected,mainly from the arable land of the Porali Plain.After drying and coning-quarter-ing,soil samples were analyzed for major and trace ele-ments using the XRF technique;sieving and hydrometric methods were employed for granulometric analysis.Esti-mated data were analyzed using Excel,SPSS,and Surfer software to calculate various indices,correlation matrix,and spatial distribution.The granulometric analysis showed that 76%of the samples belonged to loam types of soil,12%to sand type,and 8%to silt type.Weathering indices:CIA,CIW,PIA,PWI,WIP,CIX,and ICV were calculated to infer the level of alteration.These indices reflect mod-erate to intense weathering;supported by K_(2)O/AI_(2)O_(3),Rb/K_(2)O,Rb/Ti,and Rb/Sr ratios.Assessment of the geo-ac-cumulation and Nemerow Pollution indices pinpoint rela-tively high concentrations of Pb,Ni,and Cr concentration in the soils.The correlation matrix and Principal Compo-nent Analysis show that the soil in this study area is mainly derived from the weathering of igneous rocks of Bela Ophiolite(Cretaceous age)and Jurassic sedimentary rocks of Mor Range having SEDEX/MVT type mineralization.Weathering may result in the undesirable accumulation of certain trace elements which adversely affects crops.

Assessing the Variability of Extreme Weather Events and Its Influence on Marine Accidents along the Northern Coast of Tanzania

The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for reducing both the frequency of marine accidents and their associated fatalities. These fatalities include deaths, permanent disabilities and loss of properties which may result into increased poverty levels as per the sustainable development goal one (SDG1) which stipulates on ending the poverty in all its forms everywhere. Thus, in the way to support these Government efforts, the influence of climate and weather on marine accidents along Zanzibar and Pemba Channels was investigated. The study used the 10 years (2013-2022) records of daily rainfall and hourly wind speed acquired from Tanzania Meteorological Authority (TMA) (for the observation stations of Zanzibar, Pemba, Dares Salaam and Tanga), and the significant wave heights data, which was freely downloaded from Globally Forecasting System (GFS-World model of 13 km resolution). The marine accident records were collected from TASAC and Zanzibar Maritime Authority (ZMA), and the anecdotal information was collected from heads of quay and boat captains in different areas of Zanzibar. The Mann Kendal test, was used to determine the slopes and trends direction of used weather parameters, while the Pearson correlations analysis and t-tests were used to understand the significance of the underlying relationship between the weather and marine accidents. The paired t-test was used to evaluate the extent to which weather parameters affect the marine accidents. Results revealed that the variability of extreme weather events (rainfall, ocean waves and wind speed) was seen to be among the key factors for most of the recorded marine accidents. For instance, in Pemba high rainfall showed an increasing trend of extreme rainfall events, while Zanzibar has shown a decreasing trend of these events. As for extreme wind events, results show that Dar es Salaam and Tanga had an increasing trend, while Zanzibar and Pemba had shown a decreasing trend. As for the monthly variability of frequencies of extreme rainfall events, March to May (MAM) season was shown to have the highest frequencies over all stations with the peaks at Zanzibar and Pemba. On the other hand, high frequency of extreme wind speed was observed from May to September with peaks in June to July, and the highest strength was observed during 09:00 to 15:00 GMT. Moreover, results revealed an increasing trend of marine accidents caused by bad weather except during November. Also, results showed that bad weather conditions contributed to 48 (32%) of all 150 recorded accidents. Further results revealed significant correlation between the extreme wind and marine accidents, with the highest strong correlation of r = 0.71 (at p ≤ 0.007) and r = 0.75 (at p ≤ 0.009) at Tanga and Pemba, indicating the occurrence of more marine accidents at the Pemba channel. Indeed, strong correlation of r = 0.6 between extreme rainfall events and marine accidents was shown in Pemba, while the correlations between extremely significant wave heights and marine accidents were r = 0.41 (at p ≤ 0.006) and r = 0.34 (p ≤ 0.0006) for Pemba and Zanzibar Channel, respectively. In conclusion, the study has shown high influence between marine accidents and bad weather events with more impacts in Pemba and Zanzibar. Thus, the study calls for more work to be undertaken to raise the awareness on marine accidents as a way to alleviate the poverty and enhance the sustainable blue economy.

Metallurgical performance evaluation of space-weathered Chang’e-5 lunar soil

Space metallurgy is an interdisciplinary field that combines planetary space science and metallurgical engineering.It involves systematic and theoretical engineering technology for utilizing planetary resources in situ.However,space metallurgy on the Moon is challenging because the lunar surface has experienced space weathering due to the lack of atmosphere and magnetic field,making the mi-crostructure of lunar soil differ from that of minerals on the Earth.In this study,scanning electron microscopy and transmission electron microscopy analyses were performed on Chang’e-5 powder lunar soil samples.The microstructural characteristics of the lunar soil may drastically change its metallurgical performance.The main special structure of lunar soil minerals include the nanophase iron formed by the impact of micrometeorites,the amorphous layer caused by solar wind injection,and radiation tracks modified by high-energy particle rays inside mineral crystals.The nanophase iron presents a wide distribution,which may have a great impact on the electromagnetic prop-erties of lunar soil.Hydrogen ions injected by solar wind may promote the hydrogen reduction process.The widely distributed amorph-ous layer and impact glass can promote the melting and diffusion process of lunar soil.Therefore,although high-energy events on the lun-ar surface transform the lunar soil,they also increase the chemical activity of the lunar soil.This is a property that earth samples and tradi-tional simulated lunar soil lack.The application of space metallurgy requires comprehensive consideration of the unique physical and chemical properties of lunar soil.

Improving the Short-Range Precipitation Forecast of Numerical Weather Prediction through a Deep Learning-Based Mask Approach

Due to various technical issues,existing numerical weather prediction(NWP)models often perform poorly at forecasting rainfall in the first several hours.To correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting,we propose a deep learning-based approach called UNet Mask,which combines NWP forecasts with the output of a convolutional neural network called UNet.The UNet Mask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation forecasting.The overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a mask.The UNet Mask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask,which provides the corrected 6-hour rainfall forecasts.We evaluated UNet Mask on a test set and in real-time verification.The results showed that UNet Mask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI scores.Sensitivity tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNet Mask's forecast performance.This study shows that UNet Mask is a promising approach for improving rainfall forecasting of NWP models.

Digital monitoring of rotary-percussive drilling with down-the-hole hammer for profiling weathered granitic ground

Rock and geotechnical engineering investigations involve drilling holes in ground with or without retrieving soil and rock samples to construct the subsurface ground profile.On the basis of an actual soil nailing drilling for a slope stability project in Hong Kong,this paper further develops the drilling process monitoring(DPM)method for digitally profiling the subsurface geomaterials of weathered granitic rocks using a compressed airflow driven percussive-rotary drilling machine with down-the-hole(DTH)hammer.Seven transducers are installed on the drilling machine and record the chuck displacement,DTH rotational speed,and five pressures from five compressed airflows in real-time series.The mechanism and operations of the drilling machine are elaborated in detail,which is essential for understanding and evaluating the drilling data.A MATLAB program is developed to automatically filter the recorded drilling data in time series and classify them into different drilling processes in sub-time series.These processes include penetration,push-in with or without rod,pull-back with or without rod,rod-tightening and rod-untightening.The drilling data are further reconstructed to plot the curve of drill-bit depth versus the net drilling time along each of the six drillholes.Each curve is found to contain multiple linear segments with a constant penetration rate,which implies a zone of homogenous geomaterial with different weathering grades.The effect from fluctuation of the applied pressures is evaluated quantitatively.Detailed analyses are presented for accurately assess and verify the underground profiling and strength in weathered granitic rock,which provided the basis of using DPM method to confidently assess drilling measurements to interpret the subsurface profile in real time.

Unconfined compressive strength and failure behaviour of completely weathered granite from a fault zone

Understanding the strength characteristics and deformation behaviour of the tunnel surrounding rock in a fault zone is significant for tunnel stability evaluation.In this study,a series of unconfined compression tests were conducted to investigate the mechanical characteristics and failure behaviour of completely weathered granite(CWG)from a fault zone,considering with height-diameter(h/d)ratio,dry densities(ρd)and moisture contents(ω).Based on the experimental results,a regression mathematical model of unconfined compressive strength(UCS)for CWG was developed using the Multiple Nonlinear Regression method(MNLR).The research results indicated that the UCS of the specimen with a h/d ratio of 0.6 decreased with the increase ofω.When the h/d ratio increased to 1.0,the UCS increasedωwith up to 10.5%and then decreased.Increasingρd is conducive to the improvement of the UCS at anyω.The deformation and rupture process as well as final failure modes of the specimen are controlled by h/d ratio,ρd andω,and the h/d ratio is the dominant factor affecting the final failure mode,followed byωandρd.The specimens with different h/d ratio exhibited completely different fracture mode,i.e.,typical splitting failure(h/d=0.6)and shear failure(h/d=1.0).By comparing the experimental results,this regression model for predicting UCS is accurate and reliable,and the h/d ratio is the dominant factor affecting the UCS of CWG,followed byρd and thenω.These findings provide important references for maintenance of the tunnel crossing other fault fractured zones,especially at low confining pressure or unconfined condition.

Weathering Characterization and Degradation Analysis of Landscape Wooden Buildings in Semi-Arid and Sandy Area

Wooden buildings play a very important role in China’s construction and landscape architecture industry.In order to explore the weathering characteristics of the surface layer of landscape wooden buildings,the main causes of weathering were analyzed on the basis of summarizing the common types of weathering characterization.The results showed that the weathering characterization was mainly reflected in the surface defects of wood structures,such as cracking,discoloration,peeling,wind erosion wear,and so on.The coating technology on the surface of constructions was the main artificial factor affecting the surface defects of constructions.In the case of similar surface decoration conditions,sunlight and moisture were the main natural factors affecting the weathering of wooden buildings,which will promote the process of weathering.

Site observations of weathered granitic soils subjected to cementation and partial drainage using SCPTU

Because of the cementation inherited from the parent rock,weathered granitic soil is usually susceptible to disturbance,which poses considerable challenges for laboratory characterization.The cone penetration test with pore pressure measurements has long been known for its reliability in site investigations and stratigraphic profiling.However,although extensive piezocone test results and experience are available for sedimentary soil,similar advances are yet to be made for weathered granitic soil.Moreover,the experience from sedimentary soil may not be directly applicable to weathered profiles because of the essentially different natures of the two types of geomaterials.This study performs seismic piezocone tests in a weathered granitic profile comprising residual granitic soil,completely weathered granite,and highly weathered granite.Pore pressure is measured at both the cone mid-face and the shoulder,and the effects of penetrometer size and penetration rate are considered.A series of updated soil behavior type charts is proposed to interpret the test results,thereby allowing the effect of weathering to be evaluated.This paper offers an important extension to the sparse data on the in situ responses of weathered materials.

Middle Eocene terrestrial paleoweathering and climate evolution in the midlatitude Bohai Bay Basin of eastern China

The middle Eocene climatic optimum(MECO,ca.-42 Ma)is a key time period for understanding Cenozoic cooling of the global climate.Still,midlatitude terrestrial records of climate evolution during MEcO epoch are rare.In this study,continuous high-resolution record of shale sediments in mid-Eocene Shahejie Formation(MES shales)in the Bohai Bay Basin were performed with major-element and wavelet analysis.The midlatitude paleoweathering and paleoclimatic evolution during MEcO epoch were analyzed in this study.The MES shales experienced weak-moderate paleoweathering under a subtropical monsoon paleoclimate with mean annual temperature of 8.3-12.9℃ and mean annual precipitation of 685-1100 mm/yr.The MES shales record a mixed provenance involving intermediate igneous rocks,and low compositional maturity.The nutrient-rich environment led to enrichment in organic matter in the MES shales.Wavelet analysis revealed good periodicity about the paleoclimate and weathering during MECO epoch.In the stage I of MES shales depositional process,the paleolake was high in nutrients,and the MES shales experienced high chemical weathering due to a relatively warmer and more humid climate.In contrast,the climate in stage II was relatively cold and dry,and the maturity of the MES shales was relatively high during this stage,suggesting a relatively stable tectonic background.This work provides more terrestrial records of MEco epoch for midlatitude region,and is benefit for better understanding of the palaeoenvironment when MES shales formed.The implication of organic matters enrichment in this study is meaningful for the shale oil/gas exploration in Nanpu Sag.

Effect of surfactant addition on leaching process of weathered crust elution-deposited rare earth ores with magnesium sulfate

Surfactants were proposed to be added into magnesium sulfate solution to improve the leaching process of weathered crust elution-deposited rare earth ores(WREOs).Effects of surfactants and their concentration on the seepage of leaching solutions and the leaching efficiency of rare earth(RE)and aluminum(Al)were investigated,and the leaching kinetics,the mass transfer process,the adhesion work and the adhesion work reduction factor were analyzed to reveal its strengthening leaching mechanism.The results show that cetyltrimethylammonium bromide(CTAB)has a better strengthening effect on the leaching process than dodecyl trimethyl ammonium bromide(DTAB),sodium dodecyl sulfate(SDS),sodium oleate and oleic acid.In the presence of 0.04%CTAB in 0.2 mol/L solution,the permeability coefficient of WREOs increases from 0.945×10^(-5)to 1.640×10^(-5)cm·s^(-1),and the leaching efficiency of RE increases from 80%to 90%,confirming the promotion of surfactants on the leaching process of WREOs.Kinetic analysis shows that the leaching process conforms to the inner diffusion control model,and the leaching kinetics equations of RE and Al related to CTAB content are obtained.Mass transfer discussion shows a smaller height equivalent to theoretical plate(HETP)of RE and Al at CTAB content of 0.04%,suggesting the higher mass transfer efficiency here.According to the interfacial properties of leaching solutions,the calculated adhesion work and the adhesion work reduction factor further demonstrate the strengthening leaching effect of CTAB on the leaching process of WREOs.