岑参晚年“东归”去向辨

岑参从嘉州"东归",闻一多认为是回长安,陈铁民、侯忠义认为是先回河南再回长安。此两观点都有不足,岑参"东归"的真正目的地是长安,他自己所谓的"归山东"(河南)仅仅是一个偶然的且最终被放弃了的小插曲。这样,不仅解释了东归途中思归诗里的矛盾,而且还有其合情合理性。

Remote Sensing Monitoring of the Subalpine Coniferous Forests and Quantitative Analysis of the Characteristics of Succession in East Mountain Area of Tibetan Plateau——A Case Study With Zamtang County

The study area lies in the Dadu River drainage area in upstream Yangtze River.The spatial distribution of subalpine coniferous forests in 1989 and 2009 was extracted by means of a combined method of object orientation and visual interpretation,and then the overlaying analysis of these data was conducted.The type and spatial location of succession were discovered and served as the sample of dependant variable.Meanwhile,supported by GIS technology and based on DEM and thematic data,the eight variables including altitude,slope,sin and cosin of aspect,curvity of land surface,and distance to residential area,cultivated land and road were extracted served as the sample of spatial succession of subalpine coniferous forests to fit Logistic Regression,and then the contribution of each independent variable as well as the spatial property of the occurrence probability of succession was calculated.The results suggested that,during the succession of subalpine coniferous forests to meadow,the closer to the residential area and cultivated land,the greater the contribution to succession is.In particular,when the distance to the residential area decreases by one unit,the probability for its conversion to meadow will be increased by 1.15 times.During the succession of subalpine coniferous forests to deciduous-broadleaved shrubs,the sin of aspect and distance to residential area contribute more,and the probability of succession increases with increasing degree of northwardness,i.e.when the degree of northwardness increases by one unit,the probability will be increased by 1.2 times.The quantitative analysis of spatial succession property of subalpine coniferous forests will supply scientific basis to the protection and restoration of subalpine coniferous forests.

Driving Mechanism of Cultivated Land Transition in Yantai Proper,Shandong Province,China

Cultivated land transition and its driving mechanism are the hotspots among studies on land use change. In this study, we constructed a framework to study the driving mechanism of cultivated land transition from the quantitative perspective. Based on the vector data of land use in 1990, 2000 and 2009 of Yantai Proper, Shandong Province China, 11 explanatory variables were chosen from two aspects: the elevation, slope, cost distance to major water area and cost distance to minor water area, which presented physical factors; cost distance to district center, cost distance to town center, cost distance to city expansion center, cost distance to major roads, cost distance to city roads, cost distance to county roads and cost distance to rural roads, which presented the socio-economic factors. Combined with spatial analysis tools and Logistic regression analysis model, we construct Logistic regression analyses with four objectives that were urban construction land, rural residential land, orchard and other lands. The results show that, cost distance to district center, cost distance to town center, cost distance to city expansion center and cost distance to city roads are the significant explanatory variables for the transition of cultivated land into urban construction land. The main explained factors on the transition of cultivated land into rural residential land are slope, cost distance to town center, cost distance to county roads and cost distance to rural roads. Slope, cost distance to minor water area, cost distance to town center, cost distance to county roads and cost distance to rural roads are the significant explanatory variables for the transition of cultivated land into orchard land. Elevation, slope, cost distance to major water area and cost distance to minor water area are the main explanatory variables on the transition of cultivated land into other land uses.

Spatiotemporal Variations and Influencing Factors Analysis of PM_(2.5) Concentrations in Jilin Province,Northeast China

High PM_(2.5) concentrations and frequent air pollution episodes during late autumn and winter in Jilin Province have attracted attention in recent years. To describe the spatial and temporal variations of PM_(2.5) concentrations and identify the decisive influencing factors, a large amount of continuous daily PM_(2.5) concentration data collected from 33 monitoring stations over 2-year period from 2015 to 2016 were analyzed. Meanwhile, the relationships were investigated between PM_(2.5) concentrations and the land cover, socioeconomic and meteorological factors from the macroscopic perspective using multiple linear regressions(MLR) approach. PM_(2.5) concentrations across Jilin Province averaged 49 μg/m^3, nearly 1.5 times of the Chinese annual average standard, and exhibited seasonal patterns with generally higher levels during late autumn and over the long winter than the other seasons. Jilin Province could be divided into three kinds of sub-regions according to 2-year average PM_(2.5) concentration of each city. Most of the spatial variation in PM_(2.5) levels could be explained by forest land area, cultivated land area, urban greening rate, coal consumption and soot emissions of cement manufacturing. In addition, daily PM_(2.5) concentrations had negative correlation with daily precipitation and positive correlation with air pressure for each city, and the spread and dilution effect of wind speed on PM_(2.5) was more obvious at mountainous area in Jilin Province. These results indicated that coal consumption, cement manufacturing and straw burning were the most important emission sources for the high PM_(2.5) levels, while afforestation and urban greening could mitigate particulate air pollution. Meanwhile, the individual meteorological factors such as precipitation, air pressure, wind speed and temperature could influence local PM_(2.5) concentration indirectly.

Prevalence,genotypes and factors associated with HCV infection among prisoners in Northeastern Brazil

AIM： To determine hepatitis C virus （HCV） seropreva- lence and its genotypes, and to identify the factors associated with HCV infection. METHODS： This cross-sectional study, conducted in two prisons （one male and one female） in the State of Ser- gipe, Brazil, comprised 422 subjects. All of the prisoners underwent a rapid test for the detection of HCV antibod- ies. Patient~ with a positive result were tested for anti- HCV by enzyme linked immunosorbent assay and for HCV RNA by qualitative polymerase chain reaction （PCR）. The virus genotype was defined in every serum sample that presented positive for PCR-HCV. In order to determine the factors independently associated with positive serol- ogy for HCV, multivariate logistic regression was used. RESULTS： HCV seroprevalence was 3.1%. Of the 13 subjects with positive anti-HCV, 11 had viremia confirmed by PCR. Of these, 90.9% had genotype 1. A total of 43 （10.2%） were injecting drug users, and HCV seroprevalence in this subgroup was 20.6%. The variable most strongly associated with positive serology for HCV was use of injecting drugs [odds ratio （OR）, 23.3; 95% confidence interval （CI）, 6.0-90.8]. Age over 30 years （OR, 5.5; 95%CI, 1.1-29.2）, history of syphilis （OR, 9.8; 95%CI, 1.7-55.2） and history of household contact with HCV positive individual （OR, 14.1; 95%CI, 2.3-85.4） were also independently associated with HCV infection. CONCLUSION： Most of the HCV transmissions result from parenteral exposure. However, there is evidence to suggest a role for sex and household contact with an infected subject in virus transmission.

NDVI Variation of Tree Rings between March and May since 1915 in the Eastern Part of the Qilian Mountains of China

[Objective] The aim was to analyze the correlation between tree-ring width of Picea crassifolia in the east of Qilian Mountains and the precipitation, temperature and the normalized difference vegetation index (NDVI). [Method] The correlation analysis and the regression analysis were used in this study. [Result] The tree-ring width was significantly correlated with the autumn precipitation and the spring average NDVI. The conversion equation between tree-ring width and spring NDVI (R2 = 48.5% , R2adj =46.2% , F =21.627, P <0.001) was developed and NDVI sequence was reconstructed during the period 1915 -2007. The drought in the 1920s was pronounced. Vegetation cover in the Qilian Mountains increased during the period 1922-1934, 1940-1957, 1965-1969, 1984-1988 and 1995-1997, but decreased during 1935 -1939, 1958 -1964, 1970 -1983, 1989 -1994 and 1998 -2005. [Conclusion] The reconstructed NDVI showed the drought evolution in the study area.

Polymorphisms of alcohol dehydrogenase-2 and aldehyde dehydrogenase-2 and esophageal cancer risk in Southeast Chinese males

AIM： To evaluate the impact of alcohol dehydrogenase-2 （ADH2） and aldehyde dehydrogenase-2 （ALDH2） polymorphisms on esophageal cancer susceptibility in Southeast Chinese males.METHODS： Two hundred and twenty-one esophageal cancer patients and 292 healthy controls from Taixing city in Jiangsu Province were enrolled in this study. ADH2 and ALDH2 genotypes were examined by polymerase chain reaction and denaturing high-performance liquid chromatography. Unconditional logistic regression was used to calculate the odds ratios （OR） and 95% confidence interval （CI）.RESULTS： The ADH G allele carriers were more susceptible to esophageal cancer, but no association was found between ADH2 genotypes and risk of esophageal cancer when disregarding alcohol drinking status. Regardless of ADH2 genotype, ALDH2G/A or A/A carriers had significantly increased risk of developing esophageal cancer, with homozygous individuals showing higher esophageal cancer risk than those who were heterozygous. A significant interaction between ALDH2 and drinking was detected regarding esophageal cancer risk; the OR was 3.05 （95% CI： 2.49-6.25）. Compared with non-drinkers carrying both ALDH2 G/G and ADH2 A/A, drinkers carrying both ALDH2 A allele and ADH2 G allele showed a significantly higher risk of developing esophageal cancer （OR = 8.36, 95% CI： 2.98-23.46）.CONCLUSION： Both ADH2 G allele and ALDH2 A allele significantly increase the risk of esophageal cancer development in Southeast Chinese males. ALDH2 A allele significantly increases the risk of esophageal cancer development especially in alcohol drinkers. Alcohol drinkers carrying both ADH2 G allele and ALDH2 A allele have a higher risk of developing esophageal cancer.

Simplified Design Procedure of Monopile Foundation for Offshore Wind Turbine in Gujarat, India

OWTs （offshore wind turbines） are currently considered as a reliable source of renewable energy. OWT support structures account for 20%-25% of the capital cost for offshore wind installations. Pre-feasibility studies involving estimation of preliminary dimensions of the wind turbine structure need to be performed for initial costing to arrive at the commercial viability of the project. The main objective of the paper is to obtain preliminary configuration for commercial viability and approximate sizing of the foundation pile. Design equations and nomograms are proposed for quick preliminary design of monopile founded wind turbines located offshore of Gujarat. Parametric studies are carried-out on various configurations of a hollow monopile by varying water depths and properties of sand. A nonlinear static analysis of substructure is performed considering aerodynamic forces and hydrodynamic forces for various structural and soil parameters. The sub-structure design of wind turbine is based on API （American petroleum institute） standards. A simplified design methodology for monopile support structure under extreme loading condition is presented based on multivariable linear regression analysis. The input variables for the regression analysis are hydrodynamic data, angle of internal friction of sand, and the output variables are length and outer diameter of monopile. This simplified methodology is applicable in pre-studies of wind power parks.

Statistical Downscaling of FGOALS-s2 Projected Precipitation in Eastern China

A statistical regression downscaling method was used to project future changes in precipitation over eastern China based on Phase 5 of the Coupled Model Intercomparison Project （CMIPS） the Representative Concentration Pathway （RCP） scenarios simulated by the second spectral version of the Flexible Global Ocean- Atmosphere-Land System （FGOALS-s2） model. Our val- idation results show that the downscaled time series agree well with the present observed precipitation in terms of both the annual mean and the seasonal cycle. The regres- sion models built from the historical data are then used to generate future projections. The results show that the en- hanced land-sea thermal contrast strengthens both the subtropical anticyclone over the western Pacific and the east Asian summer monsoon flow under both RCPs. However, the trend of precipitation in response to warming over the 21 st century are different across eastern Chi- na under different RCPs. The area to the north of 32°N is likely to experience an increase in annual mean precipitation, while for the area between 23°N and 32°N mean precipitation is projected to decrease slightly over this century under RCP8.5. The change difference between scenarios mainly exists in the middle and late century. The land-sea thermal contrast and the associated east Asian summer monsoon flow are stronger, such that precipitation increases more, at higher latitudes under RCP8.5 compared to under RCP4.5. For the region south of 32°N, rainfall is projected to increase slightly under RCP4.5 but decrease under RCP8.5 in the late century. At the high resolution of 5 km, our statistically downscaled results for projected precipitation can be used to force hydrological models to project hydrological processes, which will be of great benefit to regional water planning and management.

Spatial Distribution Characteristics of Rocky Desertification in Qiandongnan Prefecture of Guizhou Province

Rocky desertification is a serious threat to socioeconomic development and the ecological security of karst areas. The control of rocky desertification has therefore become a major concern of both the Chinese government and local populations living in karst areas. In this paper, we used the national evaluation system for monitoring rocky desertification, and adjusted relevant indices. For example, we improved the system＇s base rock exposure index with Normalized Difference Rock index（NDRI）, substituted a soil erosion index for soil depth, and from these obtained the categories and spatial distribution of rocky desertification. We also studied the main factors and functional mechanisms of rocky desertification with consideration given to natural geographic conditions such as soil, physiognomy, elevation, slope and river network density, and, also human interference factors such as population density, GDP, population distribution, and from these got spatial distribution characteristics and influencing factors of rocky desertification in Qiandongnan prefecture. Results indicate that the primary soil types of rocky desertification in the research areas include yellow, limestone and paddy soils. These rocky desertification areas are more likely to contain limestone soil than purple soil, and least likely to contain paddy soil. The distribution of moderate or severe rocky desertification in areas with moderate to steep slope is 40%, where sloping agricultural land comprises a large proportion of the total. Rocky desertification is widely distributed in regions with precipitation between 1000–1200 mm, and this precipitation is the main factor causing greater soil erosion in limestone soil base and sloping agricultural areas. Moreover, desertification is closely related to the distribution of residential areas, population density, poverty and sloping agricultural land

COMBINING SINGULAR SPECTRUM ANALYSIS AND PAR(p) STRUCTURES TO MODEL WIND SPEED TIME SERIES

Singular spectrum analysis （SSA） is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals. Leaving out the residual components and adding up the others, the time series can be smoothed. This procedure has been used to model Brazilian electricity consumption and flow series. The PAR（p）, periodic autoregressive models, has been broadly used in modelling energy series in Brazil. This paper presents an approach of this decomposition method, by fitting the PAR（p）, considering its multivariate version known as multivariate SSA （MSSA）. The method was applied to a vector of two wind speed series recorded at two locations in the Brazilian Northeast region. The obtained results, when compared to the univariate decomposition of each series, were far superior, showing that the spatial correlation between the two series were considered by MSSA decomposition stage.

Joint statistical-dynamical approach to decadal prediction of East Asian surface air temperature

A joint statistical-dynamical method addressing both the internal decadal variability and effect of anthropogenic forcing was developed to predict the decadal components of East Asian surface air temperature(EATs)for three decades(2010–2040).As previous studies have revealed that the internal variability of EATs(EATs_int)is influenced mainly by the ocean,we first analyzed the lead-lag connections between EATs_int and three sea surface temperature(SST)multidecadal modes using instrumental records from 1901 to 1999.Based on the lead-lag connections,a multiple linear regression was constructed with the three SST modes as predictors.The hindcast for the years from 2000 to 2005 indicated the regression model had high skill in simulating the observational EATs_int.Therefore,the prediction for EATs_int(Re_EATs_int)was obtained by the regression model based on quasi-periods of the decadal oceanic modes.External forcing from greenhouse gases is likely associated with global warming.Using monthly global land surface air temperature from historical and projection simulations under the Representative Concentration Pathway(RCP)4.5 scenario of 19 Coupled General Circulation Models participating in the fifth phase of the Coupled Model Intercomparison Project(CMIP5),we predicted the curve of EATs(EATs_trend)relative to1970–1999 by a second-order fit.EATs_int and EATs_trend were combined to form the reconstructed EATs(Re_EATs).It was expected that a fluctuating evolution of Re_EATs would decrease slightly from 2015 to 2030 and increase gradually thereafter.Compared with the decadal prediction in CMIP5 models,Re_EATs was qualitatively in agreement with the predictions of most of the models and the multi-model ensemble mean,indicating that the joint statistical-dynamical approach for EAT is rational.

Representation of global precipitation anomalies using four major climate patterns

This study aims at finding the dominant climate patterns that influence the precipitation anomalies for different regions over the world. To this end, a multiple linear regression model is employed to represent the impact of four major climate patterns(El Ni?o-Southern Oscillation(ENSO), Indian Ocean Dipole(IOD), Arctic Oscillation(AO) and Antarctic Oscillation(AAO)) on the global precipitation anomalies. The normalized climate pattern indexes and normalized precipitation anomalies are used in the regression model. For the Northern Hemisphere, the three predictors used are the normalized NINO3.4 index(representing ENSO), normalized DMI(representing IOD) and normalized AO index; for the Southern Hemisphere, also three indexes are used as three predictors, and the normalized AO index is replaced by the normalized AAO index. The influences brought by each climate pattern can be represented by the magnitude of the corresponding regression coefficients, and the dominant climate patterns are those with the largest magnitude. The study results show that the precipitation anomalies in the northern part of South America and the northwestern part of Southeast Asia are mainly influenced by ENSO. The precipitation anomalies in East Africa and the southwestern part of Southeast Asia are mainly influenced by IOD. The precipitation anomalies in Europe and west coast of North America are mainly influenced by AO; the precipitation anomalies in the eastern part and southern part of South America, southern part of Africa, and the northeastern Australia are mainly influenced by AAO. These findings are consistent with the general understanding on the teleconnection features of the four climate patterns. Further, the regression model can be used for predicting precipitation anomalies through use of these major climate patterns.