Release characteristics and stabilization of heavy metals in antimony tailings in Yunnan Province,China

The pollution caused by the mining and smelting of heavy metals is becoming an increasingly severe environmental problem.In this study,the environmental risks of mine tailings were explored using typical antimony tailings(the depth of the sample taken from the ground to the deepest position of 120 cm)from the Zuoxiguo mine in Yunnan Province,Southwest China.The tailings were examined to explore the geological background,distribution characteristics,and release characteristics of heavy metals.Additionally,stabilizer treatments for heavy metals were investigated in consideration of waste treatment.The results showed that the contents of Sb and As(8.93×103 and 425 mg/kg,respectively)in the tailings were considerably higher than the local soil background values,suggesting that these metals pose a considerable threat to the surrounding environment.The geological background values of Cr,Cd,Pb,Cu,and Zn were relatively low.The results of static release showed that Sb,As,Cd,and Cr leached from the tailings more easily than Cu,Zn,and Pb under acidic conditions(pH=2.98).Geo-accumulation indices and potential ecological risk indices showed that Sb,As,Cd,and Pb were highly enriched in the tailings,whereas Cu,Cr,and Zn contents were relatively low.The single factor ecological risk index of the mining area showed that Sb and As are high ecological risk factors,whereas Cr,Cu,Zn,Cd,and Pb are not.The results of the orthogonal test results showed that by adding 15.0%(m/m)fly ash and 15.0%(m/m)zeolite powder to the quicklime and curing for 28 d,a significant stabilization effect was observed for Sb,As,and Pb.This study helps determine the priority control components for characteristic heavy metals in antimony tailings,and provides valuable insights regarding the formulation of appropriate mitigation strategies.

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.

Discovery and Analysis of Shale Gas in a Carboniferous Reservoir and its Enrichment Characteristics in the Northern Nanpanjiang Depression, Guizhou Province, China

Shale gas resources are considered to be extremely abundant in southern China,which has dedicated considerable attention to shale gas exploration in recent years.Exploration of shale gas has considerably progressed and several breakthroughs have been made in China.However,shale gas explorations are still scarce.Summary and detailed analysis studies on black shale reservoirs are still to be performed for many areas.This lack of information slows the progress of shale gas explorations and results in low quantities of stored black shale.The Carboniferous Dawuba Formation,which is widely distributed and considerably thick,is one of the black shale formations targeted for shale gas exploration in southern China in the recent years.The acquisition and analysis of total organic carbon,vitrinite reflectance,types of organic matter,mineral composition,porosity,and permeability are basic but important processes.In addition,we analyzed the microscopic pores present in the shale.This study also showesd the good gas content of the Dawuba Formation,as well as the geological factors affecting its gas content and other characteristics.To understand the prospect of exploration,we compared this with other shale reservoirs which have been already successfully explored for gas.Our comparison showesd that those shale reservoirs have similar but not identical geological characteristics.

Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia

The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection.

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linear model(SGLM),elastic net(ENET),partial least square(PLS),ridge regression,support vector machine(SVM),classification and regression trees(CART),bagged CART,and random forest(RF)for gully erosion susceptibility mapping(GESM)in Iran.The location of 462 previously existing gully erosion sites were mapped through widespread field investigations,of which 70%(323)and 30%(139)of observations were arbitrarily divided for algorithm calibration and validation.Twelve controlling factors for gully erosion,namely,soil texture,annual mean rainfall,digital elevation model(DEM),drainage density,slope,lithology,topographic wetness index(TWI),distance from rivers,aspect,distance from roads,plan curvature,and profile curvature were ranked in terms of their importance using each MLA.The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE(root mean square error),MAE(mean absolute error),and R-squared.Based on the comparisons among MLA,the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared,and was therefore selected as the best model.The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance.According to the GESM generated using RF,most of the study area is predicted to have a low(53.72%)or moderate(29.65%)susceptibility to gully erosion,whereas only a small area is identified to have a high(12.56%)or very high(4.07%)susceptibility.The outcome generated by RF model is validated using the ROC(Receiver Operating Characteristics)curve approach,which returned an area under the curve(AUC)of 0.985,proving the excellent forecasting ability of the model.The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.

Statistical functions used for spatial modelling due to assessment of landslide distribution and landscape-interaction factors in Iran

Landslides influence the capacity for safe and sustainable development of mountainous environments.This study explores the spatial distribution of and the interactions between landslides that are mapped using global positioning system(GPS) and extensive field surveys in Mazandaran Province,Iran.Point-pattern assessment is undertaken using several univariate summary statistical functions,including pair correlation,spherical-contact distribution,nearest-neighbor analysis,and O-ring analysis,as well as bivariate summary statistics,and a markcorrelation function.The maximum entropy method was applied to prioritize the factors controlling the incidence of landslides and the landslides susceptibility map.The validation processes were considered for separated 30%data applying the ROC curves,fourfold plot,and Cohen’s kappa index.The results show that pair correlation and O-ring analyses satisfactorily predicted landslides at scales from 1 to 150 m.At smaller scales,from 150 to 400 m,landslides were randomly distributed.The nearest-neighbor distribution function show that the highest distance to the nearest landslide occurred in the 355 m.The spherical-contact distribution revealed that the patterns were random up to a spatial scale of 80 m.The bivariate correlation functions revealed that landslides were positively linked to several linear features(including faults,roads,and rivers) at all spatial scales.The mark-correlation function showed that aggregated fields of landslides were positively correlated with measures of land use,lithology,drainage density,plan curvature,and aspect,when the numbers of landslides in the groups were greater than the overall average aggregation.The results of analysis of factor importance have showed that elevation(topography map scale:1:25,000),distance to roads,and distance to rivers are the most important factors in the occurrence of landslides.The susceptibility model of landslides indicates an excellent accuracy,i.e.,the AUC value of landslides was 0.860.The susceptibility map of landslides analyzed has shown that 35% of the area is low susceptible to landslides.

Flooding and its relationship with land cover change, population growth, and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve—receiver operating characteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC>90%)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the period between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72%between 2000 and 2017,while the PG rate increased by 51.68%over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.

Study on washability of microcrystal graphite using float-sink tests

This article presented an experimental research on washability of microcrystal graphite using float-sink tests.Chemical and X-ray analyses showed that graphite,semi-graphite,meta-anthracite,and anthracite existed together in this microcrystal graphite sample;and the intergrowth relationship between microcrystal graphite and gangues was very complicated based on optical mineralogy research.The results of float-sink tests revealed that:for the-25+0.5 mm size fraction,about 68%(by weight)of microcrystal graphite was obtained at the density of 2.0 g/cm^3.and the float product met the standard of commercial grade W65;for the-0.5 mm size fraction,58%(by weight)of microcrystal graphite was floated at the density of 2.0 g/cm^3,which met the standard of commercial grade W70.It can be concluded that microcrystal graphite may be upgraded by dense media separation(DMS)providing a float product using as the raw materials of casting or refractories.

Assessment of bacterial biomass in the highly contaminated urban Nanming River,Guiyang,SW China

High anthropogenic N loads and abundant bacteria are characteristic of highly contaminated urban rivers.To better understand the dispersal and accumulation of bacteria, we determined contents and isotopic compositions of suspended particulate organic matter(SPOM) and bacteria in a highly contaminated urban river(the Nanming)and effluents in winter and summer of 2013. Relative to SPOM, bacterial biomass in the river was depleted in ^(13)C and ^(15)N and its C/N ratio was lower(δ^(13)C:-33.2% ± 3.1%; δ^(15)N:-1.5% ± 1.2%; C/N:4.8 ± 0.6), while effluents showed higher ^(13)C and ^(15)N contents and C/N ratios(δ^(13)C:-25% ± 2.1%; δ ^(15)N:-8.5% ± 1.1%; C/N: 8.1 ± 1.2). Source recognition of SPOM was based on carbon isotopes because they are conservative and distinct between end-members(effluent detritus and bacterial biomass). Using a mixing model,bacterial biomass in the river was calculated to account for <20% and <56% of bulk suspended particulate organic nitrogen in winter and summer, respectively. An N budget showed that bacterial N was a small proportion of total nitrogen(<7.4%) in the riverwater.

Shale Gas Reservoir Evaluation by Geophysical Measurements:A Case Study of the Upper Ordovician–Lower Silurian in the Fenggang Block,Northern Guizhou Province

With the aid of geophysical measurements,including seventeen two-dimensional(2 D)seismic lines and the well logging curves of well FGY1,the structure and reservoir characteristics of the Upper Ordovician–Lower Silurian strata in the Fenggang block,northern Guizhou Province,were analyzed thoroughly to identify desert areas and favorable intervals.The results show that Longmaxi-Wufeng is the most prospect-rich formation,consisting of a thick succession of overmature black shale,this formation remaining partially in the Suiyang,Fenggang and Jianchaxi synclines.The Longmaxi-Wufeng shale,especially the lower member,was deposited in a reducing low-energy environment with relatively high U content and a low Th/U value.In this shale,the organic matter type(sapropelic and humic-sapropelic),total organic carbon(TOC)content,gas content,gas adsorption capacity,vitrinite reflectance and brittle mineral content are profitable for shale gas preservation and development.The fractures of this shale were closed because of its high overburden pressure.The gas adsorption capacity of this shale increases with increasing TOC content and Ro.In the Longmaxi-Wufeng Formation at well FGY1,the most favorable intervals are in the depth ranges of 2312.4–2325.1 m and 2325.8–2331.1 m.

Research on mineral processing of low quality kalium-natrium feldspar

In light of characteristic of low quality feldspar ores in Wanzai County,Jiangxi Province,steel ball was taken as grinding medium,and selective grinding could make impurity separate from feldspar effectively. Through the flow of flotation-desliming-high gradient magnetic separation,the feldspar Concentration 1 was obtained with the yield of 70.19 %,containing Fe2O3 0.17 % and TiO2 0.07 %. The feldspar Concentrate 1 could meet the quality requirement of high-grade construction ceramic. And the slime that was deslimed in the flow was also separated and recovered; the feldspar Concentration 2 was obtained with the yield of 13.18 %,containing Fe2O3 0.31 % and TiO2 0.17 %. The feldspar Concentrate 2 can be used for raw material of low-end ceramic. And gangue,with the yield of 16.63 %,was thrown away in this flow,the separating efficiency was high. Employing this flow could also bring high economy benefit and low influence of pollution to environment.

Fractal Analysis of Rainfall-Induced Landslide and Debris Flow Spread Distribution in the Chenyulan Creek Basin, Taiwan

The Chenyulan Stream in Central Taiwan follows the Chenyulan fault line which is a major boundary fault in Taiwan. In recent years, many destructive landslides have occurred in the Chenyulan Creek Basin after heavy rainfall accompanied by several strong typhoons. Three examples of landslide distributions in the Chenyulan Creek Basin, before and after 1996 and after 2004 are analyzed. The box dimension and two-point correlation dimension are employed to describe the landslide area size distribution and distance distribution between every two landslides, respectively. It is found that the number of landslides increased in this period. However, the average landslide area decreased. The correlation dimension gradually increased from 1.15 to 1.32 during this period（before and after 1996 and after 2004）. This implies that the landslide distribution in the Chenyulan Creek Basin has become diffuse and extensive. The box dimension value shows the degree of the landslide density occupied in a space. The box dimension also increased from 0.3 to 0.69 during this period. The increasing box dimension means that the landslide presented in this creek basin has gradually increased. This indicates that the slopes of this creek basin have become more unstable and susceptible.

Different Irrigation Methods and Their Comparisons Based on the Parametric Evaluation Method in Khosouyeh Dam Subbasin, Iran

Today,the world’s population is rising dramatically,and in line with this increase in the population of food and agricultural products,there must be an increasing in the number of problems associated with this process of agricultural land.Then it is necessary to use the maximum potential of this lands that product maximum yield without any damage.To reach this objective,land suitability evaluation is the most important way that can reach human to this objective.The main objective of this research was to compare different irrigation methods based on a parametric evaluation system in an area of 221402 ha in the Khosouyeh Subbasin of the Fars province,in the south of Iran.After preparing land unit map,37 points were selected for sampling.Soil properties were evaluated and analyzed.Suitability maps for drop and gravity irrigation were generated using GIS technique.The results revealed land suitability of 98.42%of the case study was classified as permanently not suitable(N2)and 1.52%currently not suitable(N1)for gravity irrigation.On the other hand,land suitability of 77.73%of the case study was classified as permanently not suitability(N2),6.05%currently not suitable(N1),12.43%marginally suitable(S3)and 3.79%moderately suitable(S2)for drop irrigation.The limiting factors for both kinds of drop and gravity irrigation are soil depth and slope of land.

Risk Assessment of Vegetation Degradation Using Geographic Information System: A Case Study of Qareh Aghaj Basin,Iran

The entire land of Southern Iran faces problems arising out of various types of land degradation of which vegetation degradation forms one of the major types. The Qareh Aghaj basin（1 265 000 ha）,which covers the upper reaches of Mond River,has been chosen for a test risk assessment of this type. The different kinds of data for indicators of vegetation degradation were gathered from the records and published reports of the governmental offices of Iran. A new model has been developed for assessing the risk of vegetation degradation. Taking into consideration nine indicators of vegetation degradation the model identifies areas with ＂Potential Risk＂（risky zones） and areas of ＂Actual Risk＂ as well as projects the probability of the worse degradation in future. The preparation of risk maps based on the GIS analysis of these indicators will be helpful for prioritizing the areas to initiate remedial measures. By fixing the thresholds of severity classes of the nine indicators a hazard map for each indicator was first prepared in GIS. The risk classes were defined on the basis of risk scores arrived at by assigning the appropriate attributes to the indicators and the risk map was prepared by overlaying nine hazard maps in the GIS. Areas under actual risk have been found to be widespread（78%） in the basin and when the risk map classified into subclasses of potential risk with different probability levels the model projects a statistical picture of the risk of vegetation degradation.