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Visible-near-infrared spectroscopy (VIRS) is one of the most promising alternative techniques for soil organic matter (SOM) due to its direct response. In this study, partial least squares regression (PLSR), support vector machine (SVM), artificial neural networks (ANNs), and Cubist combined with VIRS were utilized to develop the calibration model and evaluate the ability of machine learning models to predict soil organic matter content. A total of 190 surface soil samples (earth-cumulic-orthic anthrosols) were collected from the Weihe Plain of Shaanxi Province, China. The Kennard–Stone (KS) algorithm was employed to divide them into calibration and validation data. Moreover, the successive projections algorithm (SPA), competitive adaptive weight weighting algorithm (CARS), and their combination (SPA + CARS) were utilized to select characteristic wavelengths and improve the predictive ability of the model. Different evaluation indices, including root mean square error (RMSE), coefficient of determination (R2), the ratio of the performance to deviation (RPD), and the ratio of performance to interquartile range (RPIQ), were adopted to evaluate the accuracy of the model. In all cases, the AFS-SPA + CARS-Cubist method outperformed the PLSR, SVM, and ANN. For the Cubist model, the Rv2, RPD, and RPIQ ranged from 0.8629 to 0.9782, 0.8720 to 3.0203, and 2.005 to 4.4164, respectively. According to the results, combining VIRS with Cubist could accurately determine the SOM of earth-cumulic-orthic anthrosol soils of the Weihe Plain, China. Furthermore, SPA + CARS provided more precise calibration–validation models than SPA and CARS.

Comparing to conventional laboratory methods, visible–near-infrared reflectance (vis–NIR) spectroscopy is a more practical and cost-effective approach for estimating soil physical and chemical properties. This paper aims to build statistical machine learning models to investigate the efficiency of spectral data for comprehensive evaluation of the soil quality indicators. Seventeen physical and chemical properties were measured using standard methods as indicators of soil quality. Soil samples were scanned in the laboratory in the vis–NIR range (350–2500 nm), the calibration set of 31 samples and the validation set of 13 samples for cross-validation and independent validation; twenty-four preprocessing methods were tested to improve predictions, and a partial least squares regression (PLSR) was used to predict soil quality indicators. Comparing model indices, the model constructed based on the PLSR machine learning method has a good predictive power (R2 > 0.9, ratio of performance to deviation (RPD) > 3.0). For physical and chemical properties, the bulk density (BD, R2 = 0.97, RPD = 5.90), soil organic matter (SOM, R2 = 0.98, RPD = 8.56), pH (R2 = 0.95, RPD = 4.40), and TN (R2 = 0.98, RPD = 6.67) concentration were predicted. This indicates that the method is suitable for the prediction of these soil elements in this study area. For the heavy properties, except for Mn, Zn, Cd, and As, the other five heavy metal concentrations were well predicted. It can be seen that the prediction ability of the construction model is Hg, Cr, Pb, Ni, and Cu in order of superiority to inferiority. The results show that a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost, and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China.

The aim of this study was to screen plant species for remediation of oil-polluted soil in a semi-arid loess area. Pot experiments were conducted to test six indigenous plant species: ryegrass (Lolium perenne), tall fescue (Festuca ovina), wheatgrass (Agropyron cristatum), alfalfa (Medicago sativa), erect milkvetch (Astragalus adsurgens), and caragana (Caragana korshinskii). Loessial soil was spiked with crude oil at five pollution levels (0, 0.5, 1, 2, and 4%, w/w). We analyzed plant growth parameters at different stages, soil oxidoreductase activities, and oil degradation rate under different treatments. Soil pollution by oil negatively affected the germination rate, plant height, and biomass of all six species while inhibiting soil dehydrogenase and catalase activities. These inhibitory effects increased with increasing oil pollution level, with 2% and 1% being the critical levels at which plant growth and soil oxidoreductase activities were significantly inhibited, respectively. Both oxidoreductase activities in the rhizosphere soil were significantly higher than those in the bulk soil, which led to a considerable increase in the degradation rate of total petroleum hydrocarbons (TPHs) in the rhizosphere. The plants showed varying remediation effects in the oil-polluted soil. Erect milkvetch and caragana showed potential for remediation of soil below the 0.5% pollution level, alfalfa for soil below the 1% pollution level, and ryegrass, tall fescue, and wheatgrass for soil below the 4% pollution level. The oil pollution level was negatively correlated with plant height, biomass, oxidoreductase activities, and TPH degradation rate for the six plants (p

This paper analyzes the spatial variation in soil erosion in the loess hilly-gully region of northern Shaanxi Province, China. It sums up existing research, describes the factors that drive soil erosion, and uses geodetection to investigate the factors individually and in pairs. Our results show that soil erosion in the loess hilly-gully region of northern Shaanxi is mainly hydraulic erosion. There are significant spatial differences in the severity of soil erosion in the region. Generally, it is more severe in the north and west and less severe in the south and east. Individual factor detection results show that the major risk factors affecting soil erosion are human population distribution, precipitation, land-use type, elevation, and soil type. Interactive detection results show that interacting factors play much bigger roles in soil erosion than do individual factors. Based on forced detection results from different periods of time, we can see that forest and grass coverage, urbanization, and economic development in the study area all clearly inhibit soil erosion.

Remote sensing based vegetation index provides a practical method for the monitoring of vegetation dynamics at regional and global scales. Here, using a long-term remotely sensed normalized difference vegetation index (NDVI) dataset, we quantified the vegetation changes in the Loess Plateau (LP) over the last three decades (1982–2015), which includes the period before the Chinese“Grain for Green Program”(GGP) was launched (1982–1999), and the period after the GGP (1999–2015). The correlations between the NDVI and four climate related variables, i.e., precipitation, temperature, root-soil moisture (RSM), and a drought proxy-standardized evapotranspiration deficit index (SEDI), were also examined. The results indicated that, (i) the GGP strongly changed the vegetation in the LP. The growing-season mean NDVI (GSM-NDVI) and the annual mean NDVI (AM-NDVI) decreased slightly before the GGP launched in 1999, with slopes of −3.38 × 10−3 and − 8.00 × 10−4 year−1, respectively. However, they showed slight and significant (p

According to the specific conditions of the region, we sought to determine appropriate soil materials for improving soil conditions after the reclamation of hollow villages into cultivated land and for quickly restoring agricultural production. The test plot consisted of seven treatments with an area of 5 m × 6 m (30 m2) each, and each treatment included three replicates. The plot was filled with raw soil (old wall soil) from an abandoned homestead in Yuzihe Village, Yaotou Town, Chengcheng County, Shaanxi Province, China. The test design was completely randomised with seven treatments: fly ash (T1), organic fertiliser (chicken manure) (T2), improver (ferrous sulphate) (T3), fly ash + organic fertiliser (T4), improver + organic fertiliser (T5), fly ash + improver (T6) and no fertilisation measures (T0). The modified materials were mixed evenly with raw soil then applied to a depth of 0–30 cm onto the test plot. After adding the various modified materials, the soil bulk density decreased by 6.52–14.49% and total soil porosity, capillary porosity and non-capillary porosity increased by 7.09–15.75%, 3.14–12.67% and 15.57–38.47%, respectively. The soil permeability coefficient increased by 5.75–10.75×. Organic matter, total nitrogen, available phosphorus and available potassium in reclaimed hollow village soil increased by 12.50–66.44%, 15.15–20.00%, 6.58–64.62% and 18.24–26.82%, respectively, relative to that of T0. Only T4, T2, T6 and T5 significantly increased maize yield and water use efficiency compared with T0. The other treatments did not significantly improve maize yield or water use efficiency. Organic fertiliser and fly ash composite was an appropriate amendment for improving reclaimed soil in a hollow village in a loess hilly gully region.

The storage of soil organic carbon (SOC) should improve soil fertility. Conventional determination of SOC is expensive and tedious. Visible-near infrared reflectance spectroscopy is a practical and cost-effective approach that has been successfully used SOC concentration. Soil spectral inversion model could quickly and efficiently determine SOC content. This paper presents a study dealing with SOC estimation through the combination of soil spectroscopy and stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), principal component regression (PCR). Spectral measurements for 106 soil samples were acquired using an ASD FieldSpec 4 standard-res spectroradiometer (350–2500 nm). Six types of transformations and three regression methods were applied to build for the quantification of different parent materials development soil. The results show that (1)the basaltic volcanic clastics development of SOC spectral response bands located in 500 nm, 800 nm; Trachyte spectral response of the soil quality, and the volcanic clastics development at 405 nm, 465 nm, 575 nm, 1105 nm. (2) Basaltic volcanic debris soil development, first deviation of maximum correlation coefficient is 0.8898; thick surface soil of the development of rocky volcanic debris from bottom reflectivity logarithm of first deviation of maximum correlation coefficient is 0.9029. (3) Soil organic matter content of basaltic volcanic clastics development optimal prediction model based on spectral reflectance inverse logarithms of first deviation of SMLR. Independent variable number is 7, Rv2 = 0.9720, RMSEP = 2.0590, sig = 0.003. Trachyte qualitative volcanic clastics developed soil organic matter content of the optimal prediction model based on spectral reflectance inverse logarithms of first deviation of PLSR. Model number of the independent variables Pc = 5, Rc = 0.9872, Rc2 = 0.9745, RMSEC = 0.4821, SEC = 0.4906, forecasts determine coefficient Rv2 = 0.9702, RMSEP = 0.9563, SEP = 0.9711, Bias = 0.0637.

Microalgae possess many advantages, but the lack of a suitable strategy to simultaneously facilitate their low cost cultivation and high value productions limits their commercial applications. In this study, two microalgae strains (RT_C and RT_F) isolated from a municipal wastewater treatment plant were used to establish a two-step wastewater treatment process. During step-1, RT_C was cultivated in composite wastewater due to its high tolerance of sludge centrate; followed by step-2, in which the supernatant generated from RT_C culture was used to cultivate RT_F. The NH4+-N, PO43--P, and COD in the wastewater were removed almost completely using this strategy. Moreover, the majority of the metal ions in the wastewater were absorbed by RT_C during step-1, and thus the powdered RT_F only contained low levels of toxic metals. Our results demonstrate that this two-step process is effective for removing pollutants and while generating a powder sufficiently clean for extracting valuable compounds.

To increase reserve farmland resources, ensure food security, and to improve the ecological environment, large-scale gully regulation projects in Yan'an area of the Loess Plateau have been undertaken in China since 2013. Understanding the spatial and temporal distribution characteristics of slope soil moisture during the rainy season is, therefore, important for vegetation selection used in restoration measures in this area. The temporal stability of soil water content in the upper 100 cm soil depth in a gully regulation watershed was examined using six measurements taken during the rainy season (May–November 2016). Temporal stability analysis of the soil water content was undertaken using Spearman’s rank correlation coefficient and the relative difference method. Results showed that soil water content in the 0–50 cm and 0–100 cm soil depths demonstrated moderate temporal and spatial variability, and soil water content variability gradually decreased with increasing soil depth. Soil water content in both depths had strong temporal stability, having a positive correlation with soil depth. Furthermore, temporal stability may decrease with increasing soil water content (p

Exploring water movement characteristics in oil-polluted soils could facilitate the understanding of soil stress resistance. The aim of the present study was to evaluate the effects of oil pollution on water movement in soils with different textures and explore the underlying mechanisms. A heavy loam (Earth-cumuli-Orthic Anthrosols, EOA), a light loam (Hapli-Ustic Cambosols, HUC), and a sandy loam (Usti-Sandic Primosols, USP) were spiked with different levels of crude oil (0, 0.5, 1, 2, and 4%, w/w). Soil water movement parameters, including saturated hydraulic conductivity, unsaturated hydraulic conductivity, and specific water capacity were measured. Soil water availability under different levels of oil pollution was also analyzed. According to the results, saturated hydraulic conductivity of all three soils decreased with an increase in the level of oil pollution, as did the unsaturated hydraulic conductivity of HUC and USP. Under 4% oil pollution, the saturated hydraulic conductivity of the EOA, HUC, and USP decreased by 52.8%, 81.7%, and 39.6%, compared with their respective unpolluted soils, while the unsaturated hydraulic conductivity of HUC and USP decreased by 78.0% and 39.9% at 20% soil water content. In addition, the unsaturated hydraulic conductivity of EOA decreased with an increase in oil pollution level when the soil water content was >25%; however, a reverse trend was observed at soil water content

The degraded aeolian sandy soil in China’s Mu Us Sandy Land requires amendment before it can be suitable for maize or other agricultural production. The addition of material from the local “soft” bedrock can create a new compound soil whose particle composition and structural stability are key issues for sustainable soil development in the region. We used field data from 2010 to 2018 to study the variations in fractal characteristics of compound soil particles at soft rock to sand volume ratios of 1 : 1, 1 : 2, and 1 : 5, along with changes in soil organic matter. Over the study period, all three compound soils showed gradual increases in clay and silt content with corresponding decreasing sand content. The fractal dimension (FD) of particles at ratio 1 : 2 increased by 8.8%, higher than those at 1 : 1 (8.6%) and 1 : 5 (7.7%). The organic matter content (OMC) of particles at ratio 1 : 2 reached a maximum (6.24 ± 0.30 g/kg), an increase of 12 times over the original value. The FD and OMC of particles at ratios 1 : 1 and 1 : 5 were less stable but showed overall increase. The 1 : 2 ratio compound soil was most suitable for maize growth as its clear increase in silt and clay content most improved the texture and OMC of the original sandy soil. Such research has important theoretical and practical significance for understanding the evolutionary mechanism and sustainable use of the compound soil in agriculture within the Mu Us Sandy Land.

Soft rock efficiently retains water because it is hard when dry but expands rapidly when wet, thus it can be used as a natural water retention agent. Therefore, soft rock can be blended with sandy ...

Mining is considered to be one of the most significant sources of environmental pollution with regard to heavy metals. Mineral mining causes large quantities of mercury, cadmium, and other elements to be released into the environment and naturally poses a serious threat to environment. This paper will analyze the pollution status of agricultural soil caused by the mining of heavy metals in various mining areas in the Xunyang County in the Shaanxi Province of China, an area in famous for its resource mining. Equally, it will look at the potential ecological risk assessment process that is used to analyze the ecological risks of mining heavy metals in agricultural soil located in the surrounding areas. Based on the soil investigation, As pose a moderate ecological risk on the Au mining area. In addition, the Hg metals pose a significantly high potential ecological risk and Cd metals pose a considerable potential ecological risk on the Hg mining area. In the Pb-Zn mining area, a significantly high potential ecological risk was mainly posed by Cd. These results suggest that many heavy metals pose a high potential ecological risk on the agricultural soil in these three mining areas in the Xunyang County, and may cause elevated heavy metal contents in crops, eventually jeopardizing the health of local residents who consume food grown in polluted soil.

Effect of soft rock as soil amendment on soil moisture, water storage, saturated hydraulic conductivity, soil cone penetration resistance, bulk density, water use efficiency and yield of corn (Zeamays L.) was investigated in a field experiment in a sandy soil in Mu Us Sandy Land of China in 2012–2014. Treatments includes four rates of soft rock to sandy soil in volume (0:1, 1:1, 1:2 and 1:5) were applied only in the first year. Adding soft rock amendments increased soil moisture, soil water storage and water hold water holding capacity; decreased soil saturated hydraulic conductivity, soil cone penetration resistance and bulk density. All soft rock amendments significantly (P

The water retention characteristics of polluted soil systems are affected not only by the properties of the soil (e.g., texture) but also by those of the pollutants. This study aimed to evaluate the effect of oil pollution on the water-holding capacity of earthy materials with different textures. Three earthy materials (Lou, Loessial, and Aeolian sandy earthy materials) with different textures were treated with crude oil at five pollution levels (0, 0.5, 1, 2, and 4%). The soil water retention curve (SWRC) obtained for each treated sample was analyzed using the van Genuchten model. Oil pollution resulted in lower soil water retention in each case, with a leftward shift of the corresponding SWRC, and led to a decrease in the saturated water content of the earthy material, characterized by a marked increase in incomplete saturation, and a decrease in the residual water content (i.e., irreducible saturation). Oil pollution also determined a marked increase in the slope of the SWRC. The response of the SWRC to oil pollution was significantly influenced by the texture of earthy material, and the saturated water content of all earthy materials was strongly affected by the level of oil pollution. The residual water content of the heavier-textured Lou earthy material was also strongly affected by the oil pollution level, but no clear influence was found for the lighter-textured Loessial or Aeolian sandy earthy materials. The SWRC slope of the Aeolian sandy earthy material was sensitive to oil pollution, unlike those of the Lou and Loessial earthy materials. Oil pollution reduced the water-holding capacity of different earthy materials to an extent depending on the soil texture. Under low-suction conditions, a significant effect of oil pollution on earthy materials of different texture was generally observed, while under high-suction conditions, the effect of oil pollution was greater for heavier-textured earthy materials.

Soil organic carbon (SOC) and soil inorganic carbon (SIC) are important C pools in the Loess Plateau of Northwest China, however, variations of SOC and SIC stocks under different cultivation practices and nitrogen (N) fertilization rates are not clear in this area. A long-term field experiment started in June 2003 was conducted to investigate the SOC and SIC stocks in a calcareous soil of the Chinese Loess Plateau under four cultivation practices, i.e., fallow (FA), conventional cultivation (CC), straw mulch (SM), and plastic film-mulched ridge and straw-mulched furrow (RF), in combination with three N fertilization rates, i.e., 0 (N0), 120 (N120), and 240 (N240) kg N/hm2. Results indicate that the crop straw addition treatments (SM and RF) increased the contents of soil microbial biomass C (SMBC) and SOC, and the SOC stock increased by 10.1%–13.3% at the upper 20 cm soil depth in comparison to the 8-year fallow (FA) treatment. Meanwhile, SIC stock significantly increased by 19% at the entire tested soil depth range (0–100 cm) under all crop cultivation practices in comparison to that of soil exposed to the long-term fallow treatment, particularly at the upper 60 cm soil depth. Furthermore, moderate N fertilizer application (120 kg N/hm2) increased SOC stock at the upper 40 cm soil depth, whereas SIC stock decreased as the N fertilization rate increased. We conclude that the combined application of crop organic residues and moderate N fertilization rate could facilitate the sequestrations of SOC and SIC in the calcareous soil.

“The Belt and Road” refers to the Silk Road Economic Belt and the 21st-Century Maritime Silk Road. In this paper, the Guanzhong–Tianshui region is selected as a case study, which is located at the starting point of the Silk Road Economic Belt. First, the spatial distribution maps of plant and soil types are obtained based on the TM remote sensing images and the soil texture data. The boundaries of the third-level watershed are extracted based on the digital elevation model data. Then the corresponding mathematical models are used to evaluate the ecological water requirements of the plant–soil systems. Also, the spatial distribution characteristics are analysed at the landscape scale and the third-level watershed scale. The results show that the spatial differences of ecological water requirement in the Guanzhong–Tianshui are obvious. At the plant–soil landscape scale, the ecological water requirement per unit area of the woodland-clay loam is the highest, about 0.4777 m3/m2, while that of the grassland-gravel loam is the lowest, about 0.2813 m3/m2. At the third-level watershed scale, the ecological water requirement per unit area in the Weihe watershed (Jinghezhangjiashan above) is the highest, about 0.4123 m3/m2, while that in the Weihe watershed (Longmen to Sanmenxia) is the lowest, about 0.3002 m3/m2. The study offers scientific references for the regional ecological environment protection and ecosystem management.

The Mu Us sandy land in China’s Shaanxi Province faces a critical water shortage, with its aeolian sandy soil endangering the regional eco-environment. Here we investigated the effects of feldspathic sandstone on water retention in an aeolian sandy soil from the Mu Us sandy land. Feldspathic sandstone and aeolian sandy soil samples were mixed at different mass ratios of 0:1 (control), 1:5 (T1), 1:2 (T2), and 1:1 (T3). Soil-water characteristic curves were determined over low- to medium-suction (1–1000 kPa) and high-suction (1000–140 000 kPa) ranges, by centrifuge and water vapor equilibrium methods, respectively. Results showed that the addition of feldspathic sandstone modified the loose structure of the aeolian sandy soil mainly consisting of sand grains. The van Genuchten model described well the soil-water characteristic curves of all four experimental soils (R2-values > 0.97). Soil water content by treatment was ranked as T2 > T3 > T1 > control at the same low matric suction (1–5 kPa), but this shifted to T2 > T1 > T3 > control at the same medium- to high-suction (5–140 000 kPa). T2 soil had the largest saturated water content, with a relatively high water supply capacity. This soil (T2) also had the largest field capacity, total available water content, and permanent wilting coefficient, which were respectively 17.82%, 11.64%, and 23.11% higher than those of the control (P-values

Hydraulic properties of sandy soil from the Mu Us sandy land of Shaanxi Province were analyzed by using SEM technology. Soil porosity, the water characteristic curve, and unsaturated hydraulic conductivity of aeolian sandy soil with added soft rock were analyzed, and fractal characteristics were established. Soil hydraulic properties revealed the effect of soft rock application on soil structure and hydraulic properties. Mass ratios of soft rock to aeolian sand were 1:5, 1:2, and 1:1. Results showed that the addition of soft rock can significantly increase the bulk density of sandy soil and reduce the total porosity and macroporosity. The mass fraction of water-stable aggregates greater than 0.25mm increases significantly, increasing the fractal dimension of soil pores; reducing the soil saturated water content and saturated hydraulic conductivity. SEM technology and pore fractal theory were used to predict the soil salinity curve and unsaturated hydraulic conductivity of the improved saline soil.

Various resources from a municipal wastewater treatment plant (MWTP) are available for microalgae cultivation plants, suggesting that a combination of these technologies can be used to produce microalgae biomass and remove contaminants at a low cost. In this study, the growth performance and nutrient removal efficiency of an indigenous

The main aim of this study was to compare and evaluate the performance of fractal dimension as input data in the landslide susceptibility mapping of the Baota District, Yan&rsquo, an City, China. First, a total of 632 points, including 316 landslide points and 316 non-landslide points, were located in the landslide inventory map. All points were divided into two parts according to the ratio of 70%:30%, with 70% (442) of the points used as the training dataset to train the models, and the remaining, namely the validation dataset, applied for validation. Second, 13 predisposing factors, including slope aspect, slope angle, altitude, lithology, mean annual precipitation (MAP), distance to rivers, distance to faults, distance to roads, normalized differential vegetation index (NDVI), topographic wetness index (TWI), plan curvature, profile curvature, and terrain roughness index (TRI), were selected. Then, the original numerical data, box-counting dimension, and correlation dimension corresponding to each predisposing factor were calculated to generate the input data and build three classification models, namely the kernel logistic regression model (KLR), kernel logistic regression based on box-counting dimension model (KLRbox-counting), and the kernel logistic regression based on correlation dimension model (KLRcorrelation). Next, the statistical indexes and the receiver operating characteristic (ROC) curve were employed to evaluate the models&rsquo, performance. Finally, the KLRcorrelation model had the highest area under the curve (AUC) values of 0.8984 and 0.9224, obtained by the training and validation datasets, respectively, indicating that the fractal dimension can be used as the input data for landslide susceptibility mapping with a better effect.

Bacteria play a vital role in biotransformation of soil organic carbon (SOC). However, mechanisms of bacterium and organic carbon mineralization remain unclear during improvement of sandy soil using soft rock additions. In this study, four treatments with differing ratios of soft rock to sand of 0:1 (CK), 1:5 (C1), 1:2 (C2) and 1:1 (C3) were selected for mineralization incubation and high-throughput sequencing. The results showed that SOC, total nitrogen (TN), available phosphorus (AP), nitrate nitrogen (NO${}_{3}^{-}$-N), and mass water content (WC) of sandy soil increased significantly after addition of soft rock (P P > 0.05). The three dominant bacteria were Actinobacteria, Proteobacteria and Chloroflexi, among which Proteobacteria was negatively correlated with mineralization of organic carbon (P Cyanobacteria and Nitrospirae, respectively. Under C2 treatment, Proteobacteria and Nitrospirae had the largest increase, and Cyanobacteria had the largest decrease. Compared with other treatments, C2 treatment significantly increased bacterial diversity index, richness index and evenness index, and the richness index had a negative correlation with k value. In conclusion, when the ratio of soft rock to sand was 1:2, the k of SOC could be reduced. In addition, the retention time of SOC can be increased, and resulting carbon fixation was improved.

Understanding the spatiotemporal evolution of drought is vital for effective water resources management especially in arid and semi-arid regions and under climate change. In this study, we developed the Soil and Water Assessment Tool (SWAT)-based drought evaluation tool and used it to investigate the spatiotemporal change of drought and its driving factors over the past 50 years (1965–2014) in a typical semi-arid area, the Wei River Basin, in the Loess Plateau. The temporal trend analysis of precipitation showed an intensified hydrological cycle with a longer dry interval, and the substantially decreased wind speed resulted in a significant decrease in the evapotranspiration and a slight increase in the soil water content. The spatiotemporal analysis of drought identified the vulnerable areas and indicated that spring drought was exacerbating. Overall, this study can be informative and valuable for the drought assessment and disaster alleviation in the Loess Plateau area.

The groundwater is an important route of human exposure to kinds of contaminants. This study was carried out to investigate the occurrence and spatial distribution of groundwater arsenic in the Jinghui irrigation district in Shaanxi Province, China. The water contamination was assessed for drinking purposes by comparing it to national guidelines, and the impacts of arsenic on human health were quantified using the health risk assessment model recommended by the USEPA. The results show that the concentration of groundwater arsenic ranges from 0.0012 to 0.0190 mg/L (average 0.0054 mg/L), and 2.58% of groundwater exceed the national guidelines of 0.01 mg/L for drinking. The carcinogenic risk of arsenic affecting adults has reached 3.50 × 10−4, significantly exceeding the national guideline (1.00 × 10−4.) The health risks resulting from oral exposure are higher than those of dermal exposure. The carcinogenic risk for adults is higher than that for children, while the non-carcinogenic risk for children is higher than that for adults. The area ratio of the carcinogenic risk is 42.82%, and the area of the non-carcinogenic risk is 69.19%. Groundwater arsenic mainly originates from the discharge of industrial wastewater and the slowly release of natural sediments. The results of this study can help to set up suitable management strategies to guarantee water supply and health safety for local residents.

The parameters of water contents, oxidation-reduction potential, organic matter, total phosphorus, total nitrogen and heavy metals concentrations of mercury (Hg), cadmium (Cd), lead (Pb), arsenic (As), chromium (Cr), copper (Cu) and zinc (Zn) in surface sediment and their vertical distribution incore sediments of the Heer river were investigated. Evaluation of nutrients and heavy metals in the Heer River sediments was done by pollution index and potential ecological risk index (PERI) method, respectively. Environmental dredging depth was calculated. The parameters pH, water content, total nitrogen (TN), and organic matter (OM) as well as heavy metals decline with the increased depth. Nutrients of TN, total phosphorus (TP), OM, and heavy metals Cd, Cu, and Pb were classified into the same group and there were significant positive correlations according to the principal component analysis and Pearson correlation, implying that they have a common origin. The results of high pollution index value of OM, TP, and TN of surface sediment obtained from this research showed that the Heer River was in serious nutrient pollution. The contributions of individual metals to the potential ecological risk were in the following order: Cd > Cu > Ni > Cr > Zn > As > Pb. Cd presented serious ecological risk and contributed most to the sediments of the Heer River. The ecological risk (RI) was at a considerable high risk level, and therefore, the environmental dredging depth of the Heer River is 94 cm for the purpose of reducing heavy metal contamination of the Heer River.

Xunyang is rich in various metal minerals and is one of the four major metal mining areas in Shaanxi province, China. To explore the effects of soil heavy metals and metalloid pollution on the environment and human health around the mining areas, four places&mdash, Donghecun (D), Gongguan (G), Qingtonggou (Q) and Nanshagou (N)&mdash, were selected as the sampling sites. Potential ecological risk (PER) and health risk assessment (HRA) models were used to analyze the environmental and health risks around the mining areas. The concentration of heavy metals (Cd, Cr, Pb, Zn, Ni, Cu, Hg) and metalloid (As) in cultivated land in the vicinity of Xunyang mining areas indicated that, except for Cu, the remaining elements detected exceeded the threshold values at some sites. The geo-accumulation index (IGeo) revealed that soils in G and Q could be identified as being extremely contaminated. PER indicated that there was significantly high risk at G and Q for Hg. In N, Pb recorded the highest E r i , which also demonstrates a considerable pre-existing risk. HRA indicated that the hazard index (HI) for both carcinogenic and non-carcinogenic risks was much higher among children than among adults, and the ingestion pathway contributed the greatest risk to human health, followed by the dermal pathway and inhalation. Because the HI values of the metals and metalloid in the study areas were all lower than 1, there was no significant non-carcinogenic risk. However, the carcinogenic risk for Cr is relatively higher, surpassing the tolerable values in G, Q, and N. This study analyzed the ecological risks and human health risks of heavy metals and metalloid in farmland soils near the sampling mining areas, and demonstrated the importance of environmental changes caused by land development in the mining industry.

The temporal and spatial variation characteristics of soil moisture in typical slope and gully of Jiulongquangou small watershed were studied in the hilly and gully region of the Loess Plateau of China. The variation of soil moisture in the 0-30 cm layer on the surface of the hilly and gully region of the Loess Plateau is greater than the variation of soil moisture in each layer between 40 and 100 cm. In the study area, the model parameters such as coefficient of variation (Cv), nugget (C0), sill (C0+C), spatial degrees of freedom(C0/(C+C0)) and variable change can be used to quantitative analysis the spatial varying law. On the slope surface, the average soil water content and the coefficient of variation are negatively correlated, and can be approximated by an exponential function, while the two are positively correlated in the gully.