城市不同绿地生境小气候的时空变异规律分析

根据城市景观异质性、植被类型及遮荫水平等生态学特征在南宁市4种功能区的不同绿地,于2002年7~9月期间,用定点平行观测法进行了小气候生态因子的测定与数理统计分析。结果表明在绿地生长热季期间,南宁城市绿地的温度、湿度和光照强度等气象因子在晴稳天气下具有明显的微时空分布差异性和稳定的降温、增湿、遮阳效应,而雨天时具有一定的保温防湿效应,如在文教区绿地,林荫的温度和光照强度<林隙≤林缘≤近旁裸地或草地,湿度则与此趋势大体相反,而风速风向微空间差异性不明显。用经验的体感舒适度指数计算表明文教区与花园住宅区绿地为舒适,商业街区绿地虽也是舒适,但常略感炎热,交通干道绿地则为炎热不舒适。因此,研究还揭示了城市绿地具有明显改善城市小气候的生态效应。     

大中型农场生态经济评价指标及评价方法

本文在对几个典型大中型农场现状调查基础上，根据生态经济评价指标体系的确定原则．确立了评价农场生态经济现状的指标体系。同时，为消除不同地区间农场的资源差异，便于不同农场间的相互比较，又采用ＦＡＯ提供的生态区域法和主成分分析法对指标体系进行指标筛选，并在总结前人研究的基础上，结合农场实际，建立了能综合反映农场生态经济现状的评价方程Ｙ＝１０Ｘ＿１＋０．０７５（Ｘ＿２＋Ｘ＿３）＋０．６Ｘ＿４＋５（Ｘ＿５＋Ｘ＿６）＋２Ｘ＿７＋０．１５Ｘ＿８＋０．００１５Ｘ＿９，并把它运用到农村的生态经济评价中。本文确定的评价指标和评价方法，不仅适用于不同地区的各类农场，也适用于我国不同地区的各类农村。对我国不同地区不同类型的生态系统生态经济评价具有普遍的指导意义。     

农村环境质量区划原则及指标体系

目前工业“三废”对农村环境污染的发展趋势有所减缓，但乡镇工业对农村环境污染的比重仍在逐渐增大，污染势头继续增强。从整体上讲，农村环境质量继续恶化的趋势尚未得到有效的控制。为加强农村环境质量的管理，本文着重论述了农村环境质量区划应遵循的４条原则，以层次分析法为原理，从自然生态环境、人类开发强度、环境状况３个方面，提出由２８个指标组成的农村环境质量区划指标体系，该指标体系简便适用、易于操作，对不同层次、不同地区的具体区划具有指导意义。     

农药乙蒜素和细菌溴腈对蚕豆根尖细胞的致畸效应

利用蚕豆根尖微核技术研究农药乙蒜素和细菌溴腈对蚕豆根尖细胞的遗传损伤,测定蚕豆根尖细胞的微核率、有丝分裂指数和染色体畸变率.结果表明:2种农药均能诱发较高频率的微核率、有丝分裂指数和染色体畸变率.在乙蒜素质量浓度为0.032～0.160 g·L-1范围内,微核率随浓度升高而升高,呈一定的剂量-效应关系(r=0.881 9,P≤0.01);而在细菌溴腈质量浓度为0.625～2.5 g·L-1范围内,微核率则呈先上升后下降的趋势.     

我国东部七省生态系统对酸沉降的相对敏感性

讨论了影响亚热带生态系统对酸沉降敏感性的生态因子，提出了敏感性等级划分的指标体系，根据大量资料编制了我国东部７省１市生态系统对酸沉降相对敏感性分区图。结果表明，在研究区域内生态系统的敏感性有相当大的差别，极敏感区和敏感区面积占全区总面积的１／３以上，主要分布在福建、浙江、江西３省。     

影响高锰酸盐指数测定的主要因素

影响高锰酸盐指数测定准确性的主要因素有样品酸度、蒸馏水水质、高锰酸钾溶液浓度、水浴加热温度和滴定时间。通过环境标准样品的分析试验，考察这5个因素对高锰酸盐指数的影响程度；     

修复受污染水体的潜流人工湿地微生物多样性研究

进行了潜流人工湿地修复受污染水体试验研究,并比较单一植物与单一填料系统与多种植物系统与多种填料系统的处理效果.运用PCR-DGGE技术研究3种不同类型的潜流人工湿地中微生物种群结构,结合多样性分析和聚类分析方法,分析了人工湿地微生物种群结构特征.     

Seres2000型高锰酸盐指数仪器监测数据质量分析

水质自动站测定的高锰酸盐指数（CODMn）数据与实验室标准方法测定结果常常产生较大的偏差。作者旨在通过自动仪器与实验室标准方法的比对测试，查找自动监测结果的偏差原因，同时分析和探讨如何保障仪器的正常运行，保持自动仪器监测的数据质量，达到与实验室标准监测结果的统一。     

The impacts of different kinds of dust events on PM10 pollution in northern China

In this study the frequencies of PM₁₀ (as key urban pollutant) in 14 key environmental protection cities in northern China were analyzed. It follows that the PM₁₀ concentration in the high-frequency period is higher with an extent 0.009–0.066 mg m⁻³ than in the low-frequency period of 2001–2002. Further the impacts of three kinds of dust events on the PM₁₀ concentration in four cities (Beijing, Hohhot, Xi’an and Lanzhou) were explored. The results showed that different kinds of dust events have different influences on variation of PM₁₀ concentration in these four cities. In Lanzhou and Hohhot, which are near the source areas of dust events, the contribution degree of these three dust events to the PM₁₀ is: floating dust>dust storm>blowing dust. Whereas, in Beijing and Xi’an situated in dust event passing areas, the mean value of PM₁₀ concentration is higher in blowing dust than in floating dust (no dust storm). In addition, the influences of dust events on PM₁₀ concentration are different in the cities on different dust event paths. In Beijing and Hohhot (on the northern path), the high PM₁₀ concentration is usually caused by blowing dust. But in both Lanzhou and Xi’an (on the western/northwestern path) the high PM₁₀ pollution concentration is usually caused by floating dust.     

Geochemistry of soil around a fluoride contaminated area in Nayagarh District,Orissa, India: Factor analytical appraisal

Fluoride contamination in soil was studied in the vicinity of a hot spring in Nayagarh district of Orissa. Both bulk soil from 0 to 30 cm depth and profile soils from 0 to 90 cm depth were analyzed for total fluoride (F_t) and 0.01 M CaCl₂ extractable fluoride (F_ca), major elements, pH, EC and Organic Carbon (OC). High concentrations of both F_t and F_ca were observed in the area surrounding the hot spring and the village of Singhpur. Principal factor analysis (PFA) on the parameters of the bulk soils suggests that two major chemical processes due to three factors, control the soil geochemistry of the area. Factor-1 contributes 37.11% of the total variance and is strongly loaded with Al, Si, Fe, F_tand F_ca, and explains the fluoride enrichment of the soil, whereas the second and the third factors contribute 16.6 and 12.2%, respectively and explain the controlling process of carbonate precipitation and soil alkalinity. Multiple regression analysis of the scores of the factors was performed to derive a fluoride contamination index in soil. The magnitude of the factor effect on the contamination index follows the order of Factor-1 > Factor-2 > Factor-3. The spatial distribution of the contamination index is used to classify the area into highly contaminated, moderately contaminated and uncontaminated zones.