最低检出量确定方法的探讨

在环境监测中,由于实验条件不同等原因,有时会出现最低检出量与文献给定之间的差别。     

原子吸收法测定铜检出限的测量不确定度评估

对原子吸收法测定铜检出限的测量不确定度进行分析探讨,建立了不确定度的评估方法。影响铜检出限测量不确定度的主要因素包括标准溶液不确定度;拟合曲线不确定度;检测仪器不确定度;吸光值量化误差不确定度等,提供了上述各因素的计算方法及过程。     

重庆市主城环境空气中二氧化氮污染浅析

利用重庆市主城区2009年~2013年二氧化氮监测数据,对重庆市二氧化氮浓度时空变化趋势进行分析。结果表明:近5年二氧化氮浓度总体呈上升趋势,空间分布呈交通干线和城市中心区域二氧化氮浓度相对偏高的分布特征;日变化出现"双峰双谷"分布,峰值分别出现在中午11时左右和夜间21时左右,"双谷"分别出现在凌晨6时和下午16时左右;二氧化氮变化显示出受到交通源排放、边界层气象条件及臭氧大气光化学反应过程的影响,影响程度与道路交通相对空间位置、拥堵程度等相关联。     

浅析成都市机动车保有量的增加对大气污染物中二氧化氮浓度的影响

本文利用成都市城区近4年的环境空气二氧化氮浓度资料,结合成都市机动车保有量,分析了成都市机动车保有量的增加对空气中二氧化氮浓度的影响。分析表明,成都市机动车保有量的增加对空气中二氧化氮浓度有明显影响,尤以交通干道附近最为显著,二氧化氮浓度较非交通干线分别偏高12.0%～47.8%,成都市采取的高污染车限行等措施对二氧化氮浓度的控制起了重要作用。     

甲基橙分光光度法测定氯气分析方法的探讨

文章介绍了用甲基橙分光光度法测定氯气时,对实验中易出现的空白吸光度过高的问题提出了相应的解决方法。对此分析方法的正确使用,避免相应问题的出现,起到了一定的作用。     

水环境中氨氮的分析方法进展

氮是天然水体的重要生源要素,是水体质量评价的重要指标之一。本文重点归纳总结了近年来我国在水环境中氨氮分析方法的研究进展,内容主要包括：氮的存在形态、样品预处理和氨氮的各种分析方法,指出了未来的主攻方向、重点研究领域和发展趋势。     

实验用水对总氮检测结果的影响

用RO纯水与超纯水分别作为实验用水,通过标准曲线及空白、标准样品、水样等总氮测定对比实验,发现两种实验用水的标准曲线及空白值、标准样品的检测均受控,但两种纯水对比实验显示相对于超纯水而言,RO纯水中有一定浓度的总氮,同时在水样检测中用RO纯水作实验用水测定值明显低于用超纯水作实验用水的测定值,其差值与RO纯水中总氮值相符。实验结果表明总氮检测中不能只以标准曲线、空白值和标准样品测定值作为受控标准,要考虑实验用水中硝酸根离子的影响。     

废水总氮测定中消解损失的原因分析及解决办法

当废水样品中氨氮含量较高时,由于消解使部分氨转化成气态而损失,进而影响总氮的测定结果,本文通过对实验结果的分析,证明对样品稀释后再消解的方法进行总氮测定,可较好的解决总氮的消解损失问题.     

不同预处理对纳氏试剂比色法测氨氮的影响

用纳氏试剂比色法测污水中氨氮含量,为了消除干扰物质对测定的影响,需根据水体受污染程度,对水样通过絮凝沉淀法或蒸馏法进行预处理.本文通过试验论证了即使对较清洁的水,也以蒸馏法预处理为佳,蒸馏法预处理后的测定值更接近真值.     

气相分子吸收光谱法测定废水中的氨氮

文章应用气相分子吸收光谱仪测定废水中的氨氮,对其工作曲线、准确度、精密度、稳定性以及加标回收率进行了实验,测得该生活污水处理厂进、出口浓度分别为1.176,0.181mg/L,7次重复测定的标准偏差分别为0.448%,0.674%。在两种实际样品中分别加入0.5mg/L的标准浓度样品进行加标回收率测试,回收率分别为99.54%,99.58%。结果表明,该实验方法简便、快捷、易操作、准确度及精密度良好。与纳氏试剂法相比较具有自动化程度高、测定速度快等优点。     

纳氏试剂和校准曲线对水中氨氮测定影响分析

纳氏试剂分光光度法是油田废水中氨氮测定的经典方法,纳氏试剂的配置和校准曲线绘制是主要影响因素。文章比较了两种纳氏试剂的配置方法,得出HgCl2法纳氏试剂虽然配制麻烦,但含汞量、空白吸光度均较低,灵敏度、精密度和加标回收率较高,是氨氮测定的首选纳氏试剂。分别以氨氮的质量和质量浓度为横坐标,吸光度为纵坐标绘制两组校准曲线,得出以质量浓度为横坐标绘制的校准曲线测定结果的相对误差较小,准确度高。     

紫外法测定水质中总氮的影响因素研究

结合紫外法测定水质中总氮空白校正吸光度值易偏高、测定结果准确度低的问题,分别从检测试剂含氮量、选择实验用水、清洗玻璃器皿、改进实验操作、改进消解环境等多个方面分析了各影响因素对总氮测定的影响,相应提出了采用测定含氮量的方法筛选试剂、新制备的去离子水作为总氮实验用水、每次总氮实验使用新刷好的器皿、增加颠倒混匀步骤提高测定结果的准确度和精密度、高压蒸汽灭菌器应定时清洗换水等对策与建议。     

Application of a long-path differential optical absorption spectrometer (LP-DOAS) on the measurements of NO(2), SO(2), O(3), and HNO(2) in Gwangju, Korea

A differential optical absorption spectrometer (DOAS) technique has been applied to monitor airborne trace pollutants including NO(2), SO(2), O(3), and HNO(2) in the ultraviolet (UV) region (290-350 nm) over a 1.5 km beam path (two ways) during an intensive measurement campaign held at Gwangju, Korea (March 2002). Their mean mixing ratios (and standard deviations) were computed as 11.3 (8.8), 1.9 (1.7), 17.1 (19.3), and 0.5 (0.4)ppbv, respectively. As a means to evaluate the performance of the long-path DOAS (LP-DOAS) system with conventional point monitoring systems (PMS), correlation analysis was conducted between the two data sets. These data sets were then inspected to account for the influence of the environmental conditions on the correlation strength between the two systems, especially with respect to light level and wind speed. To facilitate the comparison, correlation analyses were conducted after dividing the data sets for those parameters into several classes. The strength of the correlations between DOAS and meteorological parameters was also examined to evaluate their effects on the DOAS performance. It was found that, among the four pollutant species, O(3) is the most sensitive to changes in meteorological conditions in relation with atmospheric mixing conditions. The overall results of our study indicate that open-path monitoring techniques can be used to effectively diagnose air quality and be substituted for the conventional point monitoring methods with the proper consideration of those parameters affecting the DOAS sensitivity (e.g., light level and wind speed).     

连续流动分析法测定水中总氮的不确定度评定

在实验室分析基础上,对连续流动分析法测定水中总氮过程的不确定度进行评定。本文建立了数学模型,对不确定度来源进行了分析,并计算了不确定度分量、合成不确定度及扩展不确定度,最后给出了总氮测量结果的标准表示方法。     

Impact of Human Activities on Carbon Dioxide (CO<Subscript>2</Subscript>) Emissions: A Statistical Analysis

Summary The balance of evidence suggests a perceptible human influence on global ecosystems. Human activities are affecting the global ecosystem, some directly and some indirectly. If researchers could clarify the extent to which specific human activities affect global ecosystems, they would be in a much better position to suggest strategies for mitigating against the worst disturbances. Sophisticated statistical analysis can help in interpreting the influence of specific human activities on global ecosystems more carefully. This study aims at identifying significant or influential human activities (i.e. factors) on CO₂ emissions using statistical analyses. The study was conducted for two cases: (i) developed countries and (ii) developing countries. In developed countries, this study identified three influential human activities for CO₂ emissions: (i) combustion of fossil fuels, (ii) population pressure on natural and terrestrial ecosystems, and (iii) land use change. In developing countries, the significant human activities causing an upsurge of CO₂ emissions are: (i) combustion of fossil fuels, (ii) terrestrial ecosystem strength and (iii) land use change. Among these factors, combustion of fossil fuels is the most influential human activity for CO₂ emissions both in developed and developing countries. Regression analysis based on the factor scores indicated that combustion of fossil fuels has significant positive influence on CO₂ emissions in both developed and developing countries. Terrestrial ecosystem strength has a significant negative influence on CO₂ emissions. Land use change and CO₂ emissions are positively related, although regression analysis showed that the influence of land use change on CO₂ emissions was still insignificant. It is anticipated, from the findings of this study, that CO₂ emissions can be reduced by reducing fossil-fuel consumption and switching to alternative energy sources, preserving exiting forests, planting trees on abandoned and degraded forest lands, or by planting trees by social/agroforestry on agricultural lands.