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Ecological vulnerability reflects the ability of an ecosystem to resist adverse factors, and has become a global research hotspot. As an energy city in China and a city in the hinterland of the Loess Plateau, the study of Taiyuan's ecological vulnerability is of great importance. Most of the studies had used individual models, whose indicator weights were subject to large human factors or large compression of indicator information. In this study, an indicator evaluation system was developed based on the pressure state response (PSR). In order to overcome the shortcomings of a single model, a hybrid data processing model based on principal component analysis-entropy method (PCA-EM) was constructed in this study to study the ecological vulnerability of the area and compared with the principal component analysis model (PCA). Meanwhile, this study used Pearson correlation coefficients for analysis to evaluate the model performance and the influence of each parameter on ecological vulnerability. The research results illustrated that from 2000 to 2019, the EVI of Taiyuan showed a trend of first increasing and then gradually decreasing, indicating that it’s ecology was gradually improving. By comparing PCA method and PCA-EM method, the PCA-EM model could more objectively and reasonably evaluate the ecological environment of the region. Most of the indicators were negatively correlated with EVI, the highest correlation coefficient between per capita arable land and EVI. Furthermore, this study proposed a hybrid model to explore the ecological vulnerability status and influencing factors of the study area, providing an idea for data processing model.

Gansu province is located at the intersection of the three plateaus(Qinghai-Tibet Plateau, Inner Mongolia Plateau, and Loess Plateau) and the three river basins(Yellow River Basin, Yangtze River Basin, and inland river basin). The complex eco-environment and climate conditions here have created rich and diverse vegetation. Therefore, it is of great significance to study the spatial distribution characteristics of rare and endangered medicinal plant resources in Gansu province for formulating reasonable protection po-licies and promoting the development of medicinal plant industry. The data of rare and endangered medicinal plant resources in 87 counties of Gansu province were collected from results of the fourth general survey. The spatial distribution and the high-or low-value gathering area of rare and endangered medicinal plant resources in Gansu province were analyzed by geostatistical methods such as exploratory spatial data analysis, trend surface analysis, and Anselin Local Moran's I. The eco-environment characteristics of the high-or low-value gathering area were analyzed with the data of vegetation type, soil texture classification, annual mean temperature, annual mean precipitation, and elevation. Furthermore, the relationships of the spatial distribution and diversity with the geographical environment of rare and endangered medicinal plants in Gansu province were analyzed to provide support for the restoration and protection policy making of these plant resources.

In the present work, an iterative method based on the four-microphone transfer matrix approach was developed for evaluating the sound speed and attenuation constant of air within a standing wave tube. In particular, when the air inside the standing wave tube is treated as the material under test, i.e., as if it were a sample of porous material, the transfer matrix approach can be used to identify the air’s acoustic properties. Note that the wavenumber within the tube is complex owing to the formation of a visco-thermal boundary layer on the inner circumference of the tube. Starting from an assumed prior knowledge of the air properties, an iterative method can be applied in the post-processing stage to estimate the complex wavenumber accurately. Experimental results presented here show that although the results are sensitive to ambient temperature, a formula previously proposed by Temkin matches closely with the measured sound speed and attenuation constant. Furthermore, it is shown that the Temkin prediction accurately represents the variation of sound speed with frequency, in contrast to the formula recommended in the ASTM E1050 standard, in which the sound speed is assumed to be independent of frequency.

As new gasification materials for gasification industry, gasification-coke can be produced with coking coal, coal by-product, and a high ratio of low-rank coal. Microscopic structure of gasificatio...

With the rapid development of urbanization, heavy metal pollution of soil has received great attention. Over-enrichment of heavy metals in soil may endanger human health. Assessing soil pollution and identifying potential sources of heavy metals are crucial for prevention and control of soil heavy metal pollution. This study introduced a spatial distribution - principal component analysis (SD-PCA) model that couples the spatial attributes of soil pollution with linear data transformation by the eigenvector-based principal component analysis. By evaluating soil pollution in the spatial dimension it identifies the potential sources of heavy metals more easily. In this study, soil contamination by eight heavy metals was investigated in the Lintong District, a typical multi-source urban area in Northwest China. In general, the soils in the study area were lightly contaminated by Cr and Pb. Pearson correlation analysis showed that Cr was negatively correlated with other heavy metals, whereas the spatial autocorrelation analysis revealed that there was strong association in the spatial distribution of eight heavy metals. The aggregation forms were more varied and the correlation between Cr contamination and other heavy metals was lower. The aggregation forms of Mn and Cu, Zn and Pb, on the other hand, were remarkably comparable. Agriculture was the largest pollution source, contributing 65.5 % to soil pollution, which was caused by the superposition of multiple heavy metals. Additionally, traffic and natural pollution sources contributed 17.9 % and 11.1 %, respectively. The ability of this model to track pollution of heavy metals has important practical significance for the assessment and control of multi-source soil pollution.

Dao-di herbs, produced in a specific region and screened through long-term clinical application, is characterized by high stable quality, good efficacy, and high popularity. With favorable climate conditions, Gansu gives birth to the Dao-di herbs Angelicae Sinensis Radix which is widely used in clinical practice, and multiple regions in Gansu, with similar ecological environment produce Angelicae Sinensis Radix. In this study, the spatial correlation and difference of phenolic acid content in Angelicae Sinensis Radix from Dao-di producing areas, emerging producing areas, and emerging planting areas in Gansu were analyzed based on ArcGIS to explore thequot;quality(chemical type)quot; characteristics of genuine Angelicae Sinensis Radix. Moreover, spatial distribution law and main driving factors of the total phenolic acid content in Angelicae Sinensis Radix in Gansu were analyzed based on geodetecctor. This study is expected to lay a basis for Dao-di research and production regionalization of Angelicae Sinensis Radix.

Southern Shaanxi is located in one of the water sources of the China's South-to-North Water Diversion Project. Evaluating ecological vulnerability can effectively supervise environmental changes and master evolving driving forces so as to guide the rational use and protection of ecological environment in regional water sources. Based on meteorological data, remote sensing images and statistical data, this study established an evaluation index system for ecological vulnerability in this special area. Taking advantage of visual expression and data mining capacity of geographical information system and remote sensing, a spatial principal component analysis method was introduced to evaluate the geospatial and temporal features of ecological vulnerability. This study established a feasible method to comprehensively assess the level of ecological vulnerability, and quantitatively analyze the driving forces that affected the ecological vulnerability in study area. The results indicated high levels of ecological vulnerability grade in the urban areas and the adjacent counties. With the accelerated urbanization, the ecological vulnerability index of the three major urban areas and their surrounding areas had gradually increasing ecological vulnerability index. The proportion of III vulnerable areas was always the largest area, which also led to an increase in the instability of the ecological environment. Over time, the most important driving force for ecological vulnerability of evaluation units in different regions was socio-economic factors, and environmental topographic factors were also important secondary factors for ecological vulnerability in Southern Shaanxi.