二噁英的研究进展

二噁英的污染给我们的生产生活带来了巨大的威胁,随着居民生活水平的日益提高,二噁英的控制问题已经越采越受到社会的关注。本文介绍了二噁英的毒性、检测技术和控制手段等方面的研究现状,并以此提出了一些解决的对策。     

机动车尾气污染控制的对策建议

近年来,南通市机动车保有量大幅增长。受机动车尾气污染影响,城市空气污染已由煤烟型污染向交通尾气型污染转变。控制机动车尾气污染,已成为大气污染防治的重点工作之一。本文根据第一次全国污染源普查结果,分析南通市机动车尾气污染和治理现状,从而提出加强机动车尾气污染控制的对策和建议。     

兰州农村面源污染现状及防治对策分析

文中在兰州统计年鉴的基础上,根据面源污染来源,分析了农业、畜牧业、生产和生活活动的危害,初步估算了主要面源污染物的流失量。并根据兰州市农村具体情况提出了相应的保护和控制措施,旨在为解决农村面源污染问题和保护农村生态环境提供借鉴。     

基于ERICA的生物辐射剂量评价

电离辐射防护不仅要保护人类而且要保护非人类生物,这是辐射防护概念的一个重大变化,也是生态保护的一大重要举措。本文概述了欧盟生物辐射剂量评价方法 FASSSET的框架体系结构、参考生物和剂量学模型。本文以某内陆核电厂为例,采用ERICA方法对该核电厂水生生物的辐射影响进行了预测计算。计算结果表明,该内陆核电厂2台百万千瓦级核电机组正常运行时对水生生物的辐射剂量率低于国际组织推荐的400μGy.h-1控制限值和ERICA的筛选剂量值10μGy.h-1。因此,该内陆核电厂不会对厂址附近水域水生生物造成不利影响。ERICA方法可作为中国现阶段开展生物辐射剂量评价的推荐方法。     

流域水资源标准体系风险分析与对策

为实施流域水资源有效保护,从技术管理层面分析流域水资源共享过程中标准体系的构成与作用。对现阶段实施的生活饮用水卫生标准、地表水环境质量标准和排水标准中涵盖的有机污染物进行对称性分析,指出标准体系中存在的技术风险以及生活污水和工业废水混排存在的管理风险。以松花江流域中某化工厂排水为实例进行有机污染物成份检测,发现了标准体系对污染物监控存在的疏漏。提出从人体健康角度完善排水和水环境质量标准,从流域水资源共享角度对排水和给水设施进行整体规划和布局,从水资源安全保障角度建立流域有机污染物优先控制名单。     

锰电解行业铬钝化剂回收利用技术研究

以某电解锰工艺末端铬钝化废水为研究对象,采用氧化、除杂、反应制得成品,污水达标处理及回用的组合工艺,制备达到国家标准的中铬黄产品,以实现铬的回收利用。结果表明:对于H2O2氧化工艺,选用V(水样)/V(30%H2O2)为(600～1000):1;可采取加碱沉淀Mn的工艺去除杂质金属离子;铬酸铅的生成反应适宜温度为55～60℃,硝酸铅投加量为理论值的1.05倍～1.10倍;采取加石灰调pH、投加硫酸铝的方法可去除水样中剩余铅离子,V(上清液)/V(5%硫酸铝溶液)为1000～2000较合适;生产该产品基本上能够做到保本、微利,但具有显著的环境效益和社会效益。     

Source and deposition of polycyclic aromatic hydrocarbons to Shanghai, China

Despite recent efforts to investigate the distribution and fate of polycyclic aromatic hydrocarbons (PAHs) in air, water, and soil, very little is known about their temporal change in wet deposition. As a result of increased attention to public health, a large-scale survey on the deposition flux and distribution of PAH contamination in rainwater was urgently conducted in Shanghai, China. In this study, 163 rainwater samples were collected from six sites, and 15 PAH compounds were detected by the use of a simple solid phase microextraction (SPME) technique coupled with gas chromatography-mass spectrometry. The dominant PAH species monitored were naphthalene, phenanthrene, anthracene, and fluoranthene. The concentration of total PAHs per event was between 74 and 980 ng/L, with an average value of 481 ng/L, which is at the high end of worldwide figures. The annual deposition flux of PAHs in rainwater was estimated to be 4148 kg/yr in the Shanghai area, suggesting rainfall as a major possible pathway for removing PAHs from the atmosphere. Diagnostic analysis by the ratios of An/178 and Fl/Fl+Py suggested that combustion of grass, wood, and coal was the major contributor to PAHs in the Shanghai region. Back trajectory analysis also indicated that the pollutant sources could be from the southern part of China.     

徐州市生态市建设存在的问题及对策探讨

分析了徐州市在建设生态城市中所面临的问题,从生态环境的管理和保护、循环经济的发展、城市基础设施的改造、城市绿化系统的建设等方面提出了相应的建议和措施,为生态城市的建设提供依据和借鉴。     

A land use regression for predicting NO2 and PM10 concentrations in di erent seasons in Tianjin region, China

Land use regression (LUR) model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression (MLR) equations were established based on the most significant variables for NO2 in heating season (R2 = 0.74), and non-heating season (R2 = 0.61) in the whole study area; and PM10 in heating season (R2 = 0.72), and non-heating season (R2 = 0.49). Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations (resolution is 10 km). Intercepts of MLR equations were 0.050 (NO2, heating season), 0.035 (NO2, non-heating season), 0.068 (PM10, heating season), and 0.092 (PM10, non-heating season) in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 (NO2, heating season), 0.043 (NO2, non-heating season), 0.087 (PM10, heating season), and 0.096 (PM10, non-heating season). These intercept values might imply an area’s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. R2 values increased 0.09 (heating season) and 0.18 (non-heating season) for NO2, and 0.08 (heating season) and 0.04 (non-heating season) for PM10. In terms of R2, LUR model performed more e ectively in heating season than non-heating season in the study area and gave a better result for NO2 compared with PM10.