scholarly journals Insights into Distribution of Soil Available Heavy Metals in Karst Area and Its Influencing Factors in Guilin, Southwest China

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 609
Author(s):  
Fen Huang ◽  
Xiaomei Wei ◽  
Tongbin Zhu ◽  
Zhuanxi Luo ◽  
Jianhua Cao
Keyword(s):  
Heavy Metals ◽  
Influencing Factors ◽  
Calcareous Soil ◽  
Pearson Correlation ◽  
Red Soil ◽  
Karst Area ◽  
Karst Region ◽  
Available Phosphorus ◽  
Available Nitrogen ◽  
Clastic Rocks

The bioavailable contents of heavy metals in karstic soils are a subject of increasing concern since the uptake of heavy metals by plants can pose a severe threat to food safety and public health. However, the bioavailable contents of heavy metals and their effective factors are poorly understood in karst regions. Calcareous soil and red soil developed from carbonate and clastic rocks, respectively, were chosen from a typical karst region (Guilin) of southwestern China, and the total (CT) and available (CA) contents of 11 heavy metals, as well as their influencing factors in soil profiles, were investigated. The results showed that calcareous soil has greater soil organic carbon, total nitrogen, available nitrogen, available potassium, and calcium (Ca) contents than red soil, but lower available phosphorus and C:N. Acid-soluble Ca (Aca) was the dominant fraction in both types of soil. Heavy metals were highly accumulated in calcareous soil, mainly controlled by secondary enrichment in the processing of carbonate rock weathering. For the majority of metals, calcareous soil had higher CT and lower CA than red soil. According to a redundancy analysis (RDA) and Pearson correlation coefficient, the high pH and Ca content in calcareous soils were primary factors influencing both the CT and CA of the metals, especially residual Ca to CT and Aca to CA. Additionally, higher soil cation exchange capacity and clay minerals also probably improved the immobility of heavy metals.

Forestland change and soil erosion in karst watershed under the Grain to Green Project

2020 ◽  
Author(s):  
Siwen Feng ◽  
Hongya Wang ◽  
Hongyan Liu ◽  
Chenyi Zhu ◽  
Shuai Li
Keyword(s):  
Soil Erosion ◽  
Influencing Factors ◽  
Southwest China ◽  
Drainage Basin ◽  
Karst Area ◽  
Karst Region ◽  
Vegetation Growth ◽  
The Past ◽  
Pixel Scale ◽  
River Drainage Basin

<div>With the implementation of the Grain to Green Project, the vegetation growth in karst region in southwest China has increased. In order to explore whether the growth of trees can be sustained after artificial afforestation in karst area and the influence of the forestland change on soil erosion, the WaTEM/SEDEM model was used to simulate the 11 stages of annual soil erosion in the past 33 years in Chongan river drainage basin in Guizhou, and the dominant influencing factors of soil erosion change in the past 33 years were discussed based the pixel scale in this study. The results showed that the forestland increased in a fluctuating way after the conversion project, and the decrease of forestland was mainly caused by drought, especially in the area where the dolomites were distributed. Therefore, the change of forestland caused no significant improvement in soil erosion since the Grain to Green Project.</div><p><!--5f39ae17-8c62-4a45-bc43-b32064c9388a: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></p>

scholarly journals The role of serum β-trophin and endostatin in patients with polycystic ovary syndrome: Are they correlated?

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Gong ◽  
Aikmu Bilixzi ◽  
Xinmei Wang ◽  
Yanli Lu ◽  
Li Wan ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Influencing Factors ◽  
Polycystic Ovary ◽  
Pearson Correlation ◽  
Healthy Controls ◽  
Newly Diagnosed ◽  
Ovary Syndrome ◽  
Factors Affecting ◽  
Main Factors

Abstract Background It’s necessary to investigate the serum β-trophin and endostatin (ES) level and its influencing factors in patients with newly diagnosed polycystic ovary syndrome (PCOS). Methods Newly diagnosed PCOS patients treated in our hospital were selected, and healthy women who took physical examination during the same period as healthy controls. We detected and compared the related serum indicators between two groups, Pearson correlation were conducted to identify the factors associated with β-trophin and ES, and the influencing factors of β-trophin and ES were analyzed by logistic regression. Results A total of 62 PCOS patients and 65 healthy controls were included. The BMI, WHI, LH, FSH, TT, FAI, FBG, FINS, HOMA-IR, TC, TG, LDL, ES in PCOS patients were significantly higher than that of healthy controls, while the SHBG and HDL in PCOS patients were significantly lower than that of healthy controls (all p < 0.05). β-trophin was closely associated with BMI (r = 0.427), WHR (r = 0.504), FBG (r = 0.385), TG (r = 0.405) and LDL (r = 0.302, all p < 0.05), and ES was closely associated with BMI (r = 0.358), WHR (r = 0.421), FBG (r = 0.343), TC (r = 0.319), TG (r = 0.404, all p < 0.05). TG, BMI, WHR and FBG were the main factors affecting the serum β-trophin levels (all p < 0.05). FBG, TC and BMI were the main factors affecting the serum ES levels (all p < 0.05). The TG, β-trophin, ES level in PCOS patients with insulin resistance (IR) were significantly higher than that of those without IR (all p < 0.05). Conclusion Increased β-trophin is closely associated with increased ES in patients with PCOS, which may be the useful indicators for the management of PCOS.

scholarly journals Straw mulching and nitrogen application altered ammonia oxidizers communities and improved soil quality in the alkaline purple soil of southwest China

AMB Express ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Songhe Chen ◽  
Rencai Gao ◽  
Xiaoling Xiang ◽  
Hongkun Yang ◽  
Hongliang Ma ◽  
...  
Keyword(s):  
Soil Quality ◽  
Ammonia Oxidation ◽  
Management Strategies ◽  
Rflp Analysis ◽  
Available Phosphorus ◽  
Purple Soil ◽  
Ammonia Oxidizers ◽  
Nitrogen Application ◽  
Available Nitrogen ◽  
Straw Mulching

AbstractMicrobe-mediated ammonia oxidation is a key process in soil nitrogen cycle. However, the effect of maize straw mulching on the ammonia oxidizers in the alkaline purple soil remains largely unknown. A three-year positioning experiment was designed as follows: straw mulching measures as the main-plot treatment and three kinds of nitrogen application as the sub-plot treatment. We found the contents of soil organic carbon (SOC), total nitrogen (TN), available potassium (AK), available nitrogen (AN), available phosphorus (AP), and NH4+-N were increased after straw mulching and nitrogen application in alkaline purple soil, so did the amoA genes abundance of ammonia-oxidizing archaeal (AOA) and bacterial (AOB). Terminal restriction fragment length polymorphism (T-RFLP) analysis revealed that Thaumarchaeote (448-bp T-RF) was dominated the AOA communities, whereas Nitrosospira sp (111-bp T-RF) dominated the AOB communities. The community compositions of both AOA and AOB were altered by straw mulching and nitrogen application in alkaline purple soil, however, the AOB communities was more responsive than AOA communities to the straw mulching and nitrogen application. Further analysis indicated that SOC and AP were the main factors affecting the abundance and community compositions of AOA and AOB in alkaline purple soil. The present study reported that straw mulching and nitrogen strategies differently shape the soil ammonia oxidizers community structure and abundance, which should be considered when evaluating agricultural management strategies regarding their sustainability and soil quality.

scholarly journals Reforestation Impacted Soil Heavy Metal Fractionation and Related Risk Assessment in the Karst Area, Southwest China

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 891
Author(s):  
Qian Zhang ◽  
Guilin Han ◽  
Xingliang Xu
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Ecological Risk ◽  
Southwest China ◽  
Risk Index ◽  
Contamination Factor ◽  
Karst Area ◽  
Risk Assessment Code ◽  
Bcr Sequential Extraction ◽  
Soil Heavy Metals

Human agricultural activities have resulted in widespread land degradation and soil contamination in the karst areas. However, the effects of reforestation after agricultural abandonment on the mobility risks and contamination of heavy metals have been rarely reported. In the present study, six soil profiles were selected from cropland and abandoned cropland with reforestation in the Puding karst regions of Southwest China. The Community Bureau of Reference (BCR) sequential extraction method was used to evaluate the compositions of different chemical fractions of soil heavy metals, including Fe, Mn, Cr, Zn, Ni, and Cd. The total contents of Cr, Ni, Zn, Cd, and Mn in the croplands were significantly higher than those in the abandoned croplands. For all soils, Cr, Ni, Zn, and Fe were mainly concentrated in the residual fractions (>85%), whereas Mn and Cd were mostly observed in the non-residual fractions (>65%). The non-residual fractions of Cd, Cr, Ni, and Zn in the croplands were higher than those in the abandoned croplands. These results indicated that the content and mobility of soil heavy metals decreased after reforestation. The individual contamination factor (ICF) and risk assessment code (RAC) showed that Cd contributed to considerable contamination of karst soils. The global contamination factor (GCF) and potential ecological risk index (RI) suggested low contamination and ecological risk of the investigated heavy metals in the croplands, moreover they can be further reduced after reforestation.

Analysis of Ecological Service Value in Vulnerable Karst Area of Southwestern Guizhou

2011 ◽  
Vol 347-353 ◽  
pp. 2241-2244
Author(s):  
Feng Tai Zhang ◽  
La Chun Wang ◽  
Wei Ci Su ◽  
Yu Hua Liang ◽  
Ji Xin Shao ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Ecosystem Service ◽  
Local Economy ◽  
Karst Area ◽  
Karst Region ◽  
Ecosystem Service Value ◽  
Rocky Desertification ◽  
Service Value ◽  
Unit Value ◽  
Southwestern Guizhou

The domestic and foreign evaluations of ecosystem service value are difficult to draw on results accepted by the public and academia. This reflects the research methods are still not mature, need to continue to be improved. In this paper, an attempt has been made to give urban unit value of ecosystem services and set up the values per unit area in southwestern Guizhou of China, in accordance with unit value of global ecosystem services developed by Costanza, et al., Chinese one by Xie, et al. and the actual situation of karst region. The analysis revealed that in the study area, the total ecosystem service value is $1.876×109 in 2006, equivalent to 104.3% of 2006 GDP (Gross Domestic Product), $1.799×109（1US$=7.8136，2006）. If the rocks change into forest in the study area, ecosystem service value will add $0.221×109, equivalent to 12.28% of GDP in 2006. Therefore, we conclude that the ecosystem services value is higher, compared to the local economy. In addition, the rocky desertification area is larger, and has already seriously influenced ecosystem service function. The tasks of ecological environment protection, propaganda and education in this region are of great significance.

scholarly journals Effect of biofertilizers on soil microbial count, nutrient availability and uptake under november sown onion

2017 ◽  
Vol 9 (1) ◽  
pp. 55-59
Author(s):  
Dilpreet Talwar ◽  
Kulbir Singh ◽  
Jagdish Singh
Keyword(s):  
Nutrient Availability ◽  
Arbuscular Mycorrhizae ◽  
N Uptake ◽  
Nutrient Status ◽  
Recommended Dose ◽  
Available Phosphorus ◽  
Microbial Count ◽  
Available Nitrogen ◽  
Soil Microbial ◽  
The Impact

Biofertilizers improves the soil microbial content, Soil nutrient status and nutrient uptake by plant. In an experiment, fifteen treatments comprised of various combinations of biofertilizers, organic manures and chemical fertilizers were compared to access the impact of different sources of nutrient on performance of onion. The highest soil organic carbon (0.40%) was observed in the treatments T12 (Farm Yard Manure (FYM) @ 20 t/ha) and T11 (FYM myctes count (29.9 X 104) was recorded in T11 (FYM @ 20 t/ha + Azotobacter + VAM) treatment while highest fungal @ 20 t/ha + Azotobacter + Vesicular-Arbuscular Mycorrhizae (VAM)). Highest bacterial (24.5 X 106) and actino-count (17.5 X 103) was observed in T3 (Azospirillium+ Recommended dose of NPK) treatment. At the time of harvesting, available nitrogen (N), available phosphorus (P) and available potassium (K) were higher in treatment T3 (Azospirillium + Recommended dose of NPK), T9 (Azotobacter+ VAM + Recommended dose of NPK) and T13 (Poultry treatment (162.6 Kg ha-1) as compared to all other treatments except T1 and T9 treatments while P uptake (13.6 Kg ha-Manure @ 5t/ha) treatments respectively than that in other treatments. Azospirillum and Azotobacter application along with recommended dose of N, P and K improved the fertility status of soil. The N uptake was significantly higher in T3 treatments. The present study highlights the need of use of biofertilizers along with organic and inorganic 1) was significantly higher in T9 treatment than that in other treatments except T1, T3, T5 and T7 treatments. The K uptake was significantly higher in T3 treatment (126.9 Kg ha-1) as compare to all other treatments except T1 and T9 manures/fertilizer to enhance the nutrient availability and improve soil health.

scholarly journals Effects of Different Straw Biochar Combined With Microbial Inoculants on Soil Environment of Ginseng

2021 ◽  
Author(s):  
Yuqi Qi ◽  
Haolang Liu ◽  
Jihong Wang ◽  
Yingping Wang
Keyword(s):  
Ph Value ◽  
Shannon Index ◽  
Available Phosphorus ◽  
Continuous Cropping ◽  
Corn Stalk ◽  
Soil Environment ◽  
Available Nitrogen ◽  
Combined Application ◽  
Ginseng Soil ◽  
Important Cash Crop

Abstract Ginseng is an important cash crop. The long-term continuous cropping of ginseng causes the imbalance of soil environment and the exacerbation of soil-borne diseases, which affects the healthy development of ginseng industry. In this study, ginseng continuous cropping soil was treated with microbial inocula using broad-spectrum biocontrol microbial strain Frankia F1. Wheat straw, rice straw and corn straw were the best carrier materials for microbial inoculum. After treatment with microbial inoculum prepared with corn stalk biochar, the soil pH value, organic matter, total nitrogen, available nitrogen, available phosphorus, and available potassium were increased by 11.18%, 55.43%, 33.07%, 26.70%, 16.40%, and 9.10%, the activities of soil urease, catalase and sucrase increased by 52.73%, 16.80% and 43.80%, respectively. A Metagenomic showed that after the application of microbial inoculum prepared fromwith corn stalk biochar, soil microbial OTUs, Chao1 index, Shannon index, and Simpson index increased by 19.86%, 16.05%, 28.83%, and 3.16%, respectively. Three classes (Alphaproteobacteria, Gammaproteobacteria and Sphingobacteria) were the dominant bacteria in ginseng soil, and their abundance increased by 7.87%, 9.81% and 1.24%, respectively, after treatment with microbial inoculum (corn stalk biochar). Results indicated that the most effective treatment in ginseng soil ould be the combined application of corn stalk biochar and Frankia F1.

scholarly journals Evaluation of Landslide Susceptibility of the Ya'an-Linzhi Section of the Sichuan-Tibet Railway based on Deep Learning

2021 ◽  
Author(s):  
Shibao Wang ◽  
Jianqi Zhuang ◽  
Jiaqi Mu ◽  
Jia Zheng ◽  
Jiewei Zhan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Influencing Factors ◽  
Landslide Susceptibility ◽  
Pearson Correlation ◽  
Confusion Matrix ◽  
Tibet Plateau ◽  
Validation Data ◽  
Landslide Hazards ◽  
Qinghai Tibet Plateau

Abstract The Qinghai-Tibet Plateau is one area with the most frequent landslide hazards due to its unique geology, topography, and climate conditions, posing severe threats to engineering construction and human settlements. The Sichuan-Tibet Railway that is currently under construction crosses the Qinghai-Tibet Plateau; there are frequent landslide disasters along the line, which seriously threaten the construction of the railway. This paper applied two deep learning (DL) algorithms, the convolutional neural network (CNN) and deep neural network (DNN), to landslide susceptibility mapping of the Ya’an-Linzhi section of the Sichuan-Tibet Railway. A geospatial database was generated based on 587 landslide hazards determined by Interferometric Synthetic Aperture Radar (InSAR) Stacking technology, field geological hazard surveys, and 18 landslide influencing factors were selected. The landslides were randomly divided into training data (70%) and validation data (30%) for the modeling training and testing. The Pearson correlation coefficient and information gain method were used to perform the correlation analysis and feature selection of 18 influencing factors. Both models were evaluated and compared using the receiver operating characteristic (ROC) curve and confusion matrix. The results show that better performance in both the training and testing phases was provided by the CNN algorithm (AUC = 0.88) compared to the DNN algorithm (AUC = 0.84). Slope, elevation, and rainfall are the main factors affecting the occurrence of landslides, and the high and very high landslide susceptibilities were primarily distributed in the Jinsha, Lancang, and Nujiang River Basins along the railway. The research results provide a scientific basis for the construction of the Ya'an-Linzhi section of the Sichuan-Tibet Railway within the region, as well as the disaster prevention and mitigation work during future safe operations.

Sign in / Sign up

Export Citation Format

Share Document