木本植物对大气污染指示与监测作用研究

本文研究了木本植物的不同部位即叶片、枝条和树皮以及植物的生理指标如气孔阻力对大气S02、TSP和重金属污染的指示和监测作用，并用树木年轮指示大气污染的历史和程度。结果认为： 承德市大气污染自1703年城市化以来开始出现，但达到严重污染水平则出现在本世纪50年代以来尤其是最近10-20年城市化与工业化的加剧，主要污染物以S02为主，从避暑山庄修建前的<0.1μg m-3达到目前的30μg m-3，重金属污染Fe自1927-45大庙铁矿开采后出现，Mn、Ni、Pb等出现在工业化以来的最近40-50年中，上述污染物含量在木质部年轮中明显升高，如S增加了10倍以上，Pb增加了560% (P<0.00l)。 不同城市功能区树皮pH和气孔阻力不同，主要与大气中的S02和TSP有关，据此可监测大气S02和TSP污染。前者以榆树、加拿大杨、垂柳和国槐最佳，相关系数分别可达-0.8384 (P<0.0l)，-0.7447、-0.6904和-0.6552 (P<0.05);后者则以白腊和旱柳下表皮最好，相关系数达0.9968和0.9951 (P<0.00l)。在扫描电镜下发现气孔受大气TSP影响出现不同程度的堵塞现象，主要有2种途径，小型颗粒物(<5μm)进入气孔腔，大型颗粒物(>30μm)可将气孔封盖。 植物不同器官部位污染物含量以树皮为最高，其次是枝条或叶，因而适宜的指示或监测部位是叶或枝条。主分量分析认为：承德市大气污染物以S为主，重金属Fe、Zn、Mn也有一定的贡献，Pb仅出现在繁忙道路区。不同季节污染物含量变化以休眠期最高，生长初期次之，生长旺盛期最低，如S和Pb分别从0.75 mg g-1和0.7 mg g-1上升到1.5 mg g-1和2.0 mg g-1(P<0.001)。植物不同季节污染物含量的变化反应了大气污染物季节变化特点，因而可以指示或监测大气污染尤其是S02污染。其中刺槐多部位复相关模型监测效果最佳，复相关系数可达0.987;某些植物单一部位的监测作用也较好，叶以珍珠梅最佳，相关系数为0.8695 (P<0.001)，枝以油松、珍珠梅、垂柳为好(r≥0.8，P <0.001)，树皮以刺槐为佳，r=0.8615 (P<0.0l)。植物不同部位的污染物含量还可用来评价大气环境质量，其中复合污染指数可以 评价总的大气环境质量，S污染指数和重金属污染指数可以评价S02、重金属和TSP污染，与直接利用污染物浓度法基本一致。油松不同部位对于大气S02的指示作用可表现为年轮对大气污染历史的指示或监测，针叶对现状S02污染的预测，并利用针叶对于S02的监测结果，绘制了大气S02污染分布图。 总之，本文利用古松年轮和现状城市植物的枝条、叶和树皮中的污染物含量以及树皮酸度等不同方面的指标，对承德市大气污染的历史和现状进行了指示与监测，即承德市大气污染从过去到现在均以S02为主，植物不同部位可以非常有效地进行大气S02污染的监测与评价，其中多部位的复相关模型预测效果极佳。另外，由植物监测而绘制的大气S02分布图，较准确地揭示了承德市大气S02现状分布规律。

KARYOTYPE AND BANDING-PATTERNS OF FEA TREE RAT (CHIROMYSCUS-CHIROPUS)

Fea's tree rat (Chiromyscus chiropus) is a very rare species which there are only a few specimens in the world. The chromosomes of two male specimens, collected from Xishuanbanna, Yunnan, are analysed by several banding technique (G-, C-bands, as well as Ag-staining). The diploid chromosome number is 22, and autosomes comprise 5 pairs of metacentrics, 2 pairs of subacrocentrics, and 3 pairs of acrocentrics. The X chromosome is a acrocentric, and Y is a micro-chromosome, almost a point, which could be a marker chromosome of the species and the genus. The centromeric C-bands are very faint, and C-bands of Nos. 1, 2, 9 and Y chromosome are negative. Only one pair Ag-NORs was found on No. 10 in the silver-stained karyotype. The relationship between morphologic and chromosomal features was discussed, and C-banded karyotype evolutionary trend has also been discussed. Moreover, the conventional karyotype of Niviventer confucianus was described.

Effects of gamma-aminobutyric acid, progesterone and ionophore A23187 on acrosome reaction of tree shrew sperm in vitro: examination of acrosome reaction with an improved fluorescence microscopy

A number of acrosome reaction (AR) initiators have been found to be effective in inducing AR of human, laboratory and domestic animal sperm. Using an improved simple fluorescence microscopy, effects of gamma-aminobutyric acid (GABA), progesterone and ionophore A23187 on sperm AR of tree shrew, a useful animal model in biomedical research, have been investigated. Spontaneous AR in 4.92-7.53% of viable sperm was observed. Complete AR in 10.31-18.25% of viable tree shrew sperm was obviously induced by 5 mu M and 10 mu M calcium ionophore A23187, 1 mM GABA, and 5 mu M progesterone, and there were no significant differences between their abilities to initiate complete AR. No significant differences of AR percentages between 1- and 2-h treatments with A23187, progesterone and/or GABA were observed. These results suggested that the responses of tree shrew sperm to these AR initiators are similar to that of human and other mammalian sperm. (C) 1997 Elsevier Science B.V.

Decision tree classification of land cover from remotely sensed data

Decision tree classification algorithms have significant potential for land cover mapping problems and have not been tested in detail by the remote sensing community relative to more conventional pattern recognition techniques such as maximum likelihood classification. In this paper, we present several types of decision tree classification algorithms arid evaluate them on three different remote sensing data sets. The decision tree classification algorithms tested include an univariate decision tree, a multivariate decision tree, and a hybrid decision tree capable of including several different types of classification algorithms within a single decision tree structure. Classification accuracies produced by each of these decision tree algorithms are compared with both maximum likelihood and linear discriminant function classifiers. Results from this analysis show that the decision tree algorithms consistently outperform the maximum likelihood and linear discriminant function classifiers in regard to classf — cation accuracy. In particular, the hybrid tree consistently produced the highest classification accuracies for the data sets tested. More generally, the results from this work show that decision trees have several advantages for remote sensing applications by virtue of their relatively simple, explicit, and intuitive classification structure. Further, decision tree algorithms are strictly nonparametric and, therefore, make no assumptions regarding the distribution of input data, and are flexible and robust with respect to nonlinear and noisy relations among input features and class labels.

CONFORMATION OF PHENYL RINGS IN THE HELICAL CHAIN POLYMER POLY(TRIPHENYLMETHYL METHACRYLATE)

The conformation of phenyl rings in the side groups of the helical chain polymer poly(tripenyl-methyl methacrylate) (1) in solution was studied by spectroscopic methods. According to the Raman spectrum the phenyl rings of 1 and triphenylmethyl methacrylate in solution have the same depolarization ratio at 1002 cm-1. The electronic spectra (ultraviolet and fluorescence) of 1 are similar to those of model substances, except for the "red shift" of the spectra of about 5 nm. It was concluded that the phenyl rings can rotate around the phenyl-C bond.

The Relationship between Decision Trees and the Scale of Train Data Set

Decision Trees need train samples in the train data set to get classification rules. If the number of train data was too small, the important information might be missed and thus the model could not explain the classification rules of data. While it is not affirmative that large scale of train data set can get well model. This Paper analysis the relationship between decision trees and the train data scale. We use nine decision tree algorithms to experiment the accuracy, complexity and robustness of decision tree algorithms. Some results are demonstrated.