黄海近岸底栖贝类体内典型有机污染物分布

根据第二次全国海洋污染基线凋查数据,确定黄海近岸底栖贝类体内典型有机污染物的含量、分布及其潜在的生态风险.结果表明,在35%以上的站点,物种体内石油烃含量超过国家海洋生物质量第一类标准(15 000 ng/g),大连湾附近海域贝类体内含量甚至高出第二类标准(50 000 ng/g).在威海、大连湾和胶州湾出现多环芳烃和酞酸酯少数相对高值站点(300 ng/g左右),而其它大多数站点贝类体内多环芳烃和酞酸酯的含量都较低.中、高环组分占优指示近岸海区的多环芳烃主要来源于热解过程;二丁基酞酸酯和2-乙基己基酞酸酯是酞酸酯的主要组分.在所有站点,底栖贝类体内PCBs的含量普遍很低(<10 ng/g).贝类体内DDTs含量超过第一类标准(10 ng/g)的站点大部分集中在南黄海沿岸,海州湾内一站点已超出第二类标准(100 ng/g),而组分以代谢产物DDD和DDE为主.在所有站点的贝类体内都有p,p'-DDT检出,尤其在北黄海的大连湾和蓬莱附近海区(比例高于50%),指示周边地区存在潜在的DDT输入源.据此,大连湾、威海、胶州湾沿岸贝类体内石油烃和多环芳烃含量较高,胶州湾、海州湾附近海域贝类体内DDTs含量较高,这些属于较高生态风险区,而大连湾、蓬莱近岸可能的DDT新输入对底栖环境构成一定威胁.     

中国机动车有毒有害空气污染物排放估算

有毒有害空气污染物的环境影响是国内外研究的热点,而机动车排放是其重要源之一.通过资料调研,获得各车型年均行驶里程以及有毒有害空气污染物排放与非甲烷挥发性有机物(NMVOC)排放的质量分数,梳理各车型保有量,利用COPERT Ⅳ模型计算NMVOC排放因子,建立了2005年中国分车型、分省域的有毒有害空气污染物排放清单.结果表明:2005年中国苯,1,3-丁二烯,甲醛,乙醛和丙烯醛的机动车排放总量分别为31.65×104,7.45×104,13.26×104,5.11×104和1.65×104 t,其主要来源于摩托车、汽油小客车和重型柴油货车;广东、山东、江苏、浙江、河北和河南六省排放量较高.      

Decomposition of gaseous CF2CIBr by cold plasma method

The paper presented the results regarding the decomposition of gaseous CF₂ClBr by cold plasma method.After two minutes discharge,the maximum decomposition rate of 2660 Pa CF₂ClBr pure and 2660 Pa CF₂ClBr plus 7980 Pa O₂ reached 60% and 80%,respectively.The paper also studied the cold plasma gas phase chemistry reaction mechanism of CF₂ClBr at low pressure,and the pressure effects of CF₂ClBr and added gas(He,N₂,O₂ and dry air)on the CF₂ClBr decomposition respectively by cold plasma method.These studies will be helpful to application of cold plasma method in the treatment of hazardous gaseous wastes.     

亚甲基兰分光光度法测定气态硫化氢方法研究

对气态污染物H2S测定方法亚甲基兰分光光度法进行了深入细致地探索研究,得出该方法最佳实验室测定条件,包括标准溶液和吸收溶液的选择,显色剂和样品的稳定时间,SO2、NOx对测定方法的影响以及样品的吸收效率等。实验结果表明,该方法校准曲线斜率为0.148～0.151吸光度/μgH2S·11mL,标准偏差1.2×10\+-3,环境监测样品回收率为84%～89%。     

计算机绘制大气污染物浓度分布等值线图

用计算机绘制大气污染物长期平均浓度分布等值线图，对在绘图过程中坐标系的建立，计算机间距的确定以及风向的加密等的处理方法作了讨论，并给出了绘图的基本步骤。     

Analysis of organic compounds in coal gasification wastewater

A procedure for analysis of organic pollutants in coal gasification wastewater was developed, including a series extraction steps at different pH, followed by LC separation or resin adsorption, then analyzed by GC or GC/MS. More than 200 organic pollutants in 22 categories were determined. CH2CL2 extraction at NaHCO3 presence was used to separate carboxylic acids with phenolic compounds in aqueous. Derivatization with acetic anhydride was used for analyses of mono-, di-, poly-hydroxyl phenolic compounds. 21 mono-hydroxyl phenols and 13 di-hydroxyl phenols were determined from the coai gasification wastewater samples. Derivatization with BF3-CH3OH was used for analysis of carboxylic acid. 17 mono-carboxyl, 4 di-carboxyl acids and 6 aromatic acids were determined from coal gasification wastewater samples.     

Study on gaseous CS2 using laser-induced fluorescence

ＳｔｕｄｙｏｎｇａｓｅｏｕｓＣＳ＿２ｕｓｉｎｇｌａｓｅｒ－ｉｎｄｕｃｅｄｆｌｕｏｒｅｓｃｅｎｃｅＰａｎＺｈｅ；ＺｈａｎｇＹｕｅ；ＤｅｎｇＧｕｏｈｏｎｇ；ＰａｎＸｕｎｘｉ，ＨｏｕＨｕｉｑｉ；ＬｉＣｈａｎｇｌｉｎ（ＥｎｖｉｒｏｎｍｅｎｔａｌＳｃｉｅｎｃｅ...     

超临界流体色谱分析有机污染物

概述了超临界流体色谱的进展，特点、原理及影响超临界流体色谱测定的主要因素。介绍了超临界流体色谱与其他测定仪器的联用技术。列举了该技术在分析环境样品中有机污染物的应用。如分析有机氯、有机磷和氨基酸酯及农药的残留组分，多环芳烃，芳香烃等化合物。     

长寿区饮用水源地保护对策

在分析长寿区水资源开发利用现状的基础上,按照水功能区划结果和水质目标,计算出龙溪河水源地保护区的污染物控制总量和削减量,并提出龙溪河水源地的保护对策。     

Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing, China

Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of ? 0.0043 ppb, mean absolute error of 0.4836?ppb, root mean squared error of 0.5320?ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.