Investigation of the complexation of metal-ions by strong ligands in fresh and marine water

SCOPE: The detection and investigation of metal ions bound in strong complexes in natural waters is a difficult task, due to low concentration of the metal ions themselves, and also of the strong ligands, which, moreover, are often not of a well-defined composition. Here, a method is proposed for the investigation of the speciation of metal ions in natural waters. OBJECTIVE AND METHOD: It is based on the sorption of metal ions on strongly sorbing ion exchange resins, i.e. complexing resins. For this reason the method is called Resin Titration. It has been shown in previous investigations that the concentration of metal ion totally sorbed by a particular resin, and its reaction coefficient in the solution phase in the presence of the resin, can be determined from the sorption data using a simple relationship. Here, a data treatment (the Ruzic linearization method) is proposed for also determining the concentration of the ligands responsible for the complex in equilibrium with the resin. RESULTS: The method was applied to data obtained by Resin Titration of a freshwater and a seawater. Copper(II) and aluminium(III) were considered, using Chelex 100 as a titrant, due to its strong sorbing properties towards these metal ions. The results were: the total metal concentration in equilibrium with the resin, the side reaction coefficients, and the concentration of ligands. In all these cases the ligands forming very strong complexes were found to be at concentration lower than that of the metals. CONCLUSION: The Ruzic linearization method allows the determination of the concentration of the ligands forming very strong complexes in equilibrium with Chelex 100. The reaction coefficient was better determined by the calculation method previously proposed for RT. The ligands responsible for the strong complexes were found to be at low concentration, often lower than that of the metal ions considered. The metal in the original sample is partly bound to these ligands, since the complexes are very strong. Only a part of the metal is linked to weaker ligands, or free.     

硝基苯类废水的预处理技术研究

Fe(OH)2对硝基苯具有强烈的还原作用，短时间内可把硝基苯还原为苯胺。催化铁屑法处理硝基苯类废水，除了包括铁屑法处理废水的各种反应外，还能使硝基苯在其表面直接还原，且反应在弱碱性条件下效果较好。m(Fe)：m(Cu)为10：1，pH为9．5，废水硝基苯进水质量浓度为250mg／L，反应时间为30min，硝基苯的去除率达100％。     

不同功能区空气负离子的监测分析

监测了西宁地区不同功能区空气负离子状况，以公园游览区空气负离子浓度最高，平均３５８个／ｃｍ＾３；工业区最低，平均浓度２５９个／ｃｍ＾３。各功能区空气负离子浓度明显不同，Ｐ〈０．０１。用空气负离子浓度、空气离子单级系数和安培空气质量评价指数评价各功能区空气质量，从好到差依次为：公园游览区、生活居住区、商业交通繁忙区、工业区。３种评价指标结果相同，符合一般规律。     

关中典型城市及农村夏季PM2.5的化学组成对比

于2016年7~8月采集了陕西省西安市（城市）及蔺村（农村）夏季昼夜PM_2.5样品,分析其有机碳（OC）、元素碳（EC）和无机离子等化学组分的含量,探讨关中平原城市和农村地区PM_2.5的化学组成和来源的差异.结果表明,采样期间西安和蔺村的PM_2.5浓度分别为（49.7±22.8）和（62.6±14.2）μg/m³.西安PM_2.5中OC和EC的浓度[（6.5±2.5）μg/m³,（3.2±1.8）μg/m³]与蔺村[（6.8±1.8）μg/m³,（3.8±2.3）μg/m³]相当.西安OC/EC比值白天（2.6）高于夜晚（1.9）,蔺村反之（白天：1.6;夜晚：2.7）,主要是因为夜间城市地区重型卡车运输活动增强导致排放更多EC,而夜间农村地区人为活动较少导致EC排放显著降低.西安和蔺村无机离子总浓度分别为（20.2±14.6）和（30.1±10.5）μg/m³,占PM_2.5浓度的40.6%和47.6%.蔺村SO₄^2-的平均浓度高达19.0μg/m³,占PM_2.5浓度的30%以上,远高于西安（9.4μg/m³和18.9%）,主要与农村固体燃料（煤和生物质）使用有关.西安NO₃^-和Ca²⁺的浓度及其对PM_2.5的贡献、NO₃^-/SO₄^2-比值均明显大于蔺村,表明城市地区受机动车尾气和扬尘的影响更大.西安K⁺与Ca²⁺和Mg²⁺的相关性较强,而蔺村K⁺与EC的相关性显著强于西安,说明西安市区K⁺由粉尘源主导,而农村地区则主要来自生物质燃烧.     

沈阳市PM2.5离子成分对呼吸疾病门诊数影响研究

采用时间序列的半参数广义相加模型,在控制了长期趋势、"星期几效应"和气象因素等混杂因素的基础上,分析沈阳市大气污染物及PM_2.5中水溶性离子对呼吸系统疾病门诊就诊人数的影响,并按性别和年龄分层建模.结果表明：PM_2.5及其各离子成分与呼吸系统疾病门诊人数之间存在关联,并有明显的滞后效应.受冬季供暖燃煤排放影响,PM_2.5、NO₃^-和NH₄⁺呈显著关联,在滞后累积2d后风险最大.最佳滞后时间下,PM_2.5的浓度每增加10µg/m³,对应呼吸系统疾病日门诊就诊人数增加百分比（ER）为1.31%（95% CI：1.2%~1.43%）;离子成分SO₄^2-、NO₃^-、NH₄⁺、Cl^-、K⁺、Mg²⁺、Ca²⁺和Na⁺的浓度每增加1个4分位间距（IQR）,对应的呼吸系统疾病日门诊就诊人数增加百分比（ER）分别为3.22%（95% CI：2.81%~3.62%）、4.67%（95% CI：4.13%~5.22%）、5.41%（95% CI：4.49%~6.33%）、7.38%（95% CI：3.91%~10.96%）、0.14%（95% CI：-6.34%~7.07%）、7.64%（95% CI：-11.87%~31.47%）、3.57%（95% CI：-2.83%~10.39%）和0.46%（95% CI：-16.64%~21.06%）.PM_2.5、Cl^-、Mg²⁺、Ca²⁺和Na⁺对女性呼吸疾病门诊人数的影响比对男性的影响大.PM_2.5、SO₄^2-、Cl^-、Ca²⁺和Na⁺对≥65岁的老人门诊人数的影响比对15~65岁劳动年龄人群的影响大.表明不同性别、不同年龄由于生理结构和环境因素的不同而引起的差异不同.     

异养硝化-好氧反硝化混合菌对尿素的去除及重金属和盐度的影响

为探究异养硝化-好氧反硝化混合菌对尿素的去除效果,本文考察了混合菌(DM01+YH01+YH02)对尿素的去除特性,以及重金属和盐度对其去除率的影响.结果表明,当废水中尿素质量浓度为200.0 mg·L~(-1),碳源为柠檬酸三钠、 C/N为10、温度为30℃、 pH为7和转速为130 r·min~(-1)时,尿素在24 h可以得到高效降解,去除率为91.8%;重金属离子(Ni~(2+)、Cd~(2+)、Cu~(2+)和Zn~(2+))会降低混合菌对尿素的去除效果,影响能力的大小顺序为Cd~(2+)Cu~(2+)Ni~(2+)Zn~(2+),然而, Fe~(2+)(20.0 mg·L~(-1))会增强混合菌对尿素的去除效果;盐度(10.0 mg·L~(-1))会抑制混合菌株对于尿素的去除.     

成都夏冬季PM2.5中水溶性无机离子污染特征

利用大气细颗粒物水溶性组分及气态前体物在线监测设备(GAC-IC)对成都市2017年夏、冬两季大气PM_(2.5)中水溶性无机离子(WSIIs)及气态前体物进行了连续观测,对其污染特征及冬季一次典型污染过程进行了深入分析.结果表明,成都冬季PM_(2.5)质量浓度为100.2μg·m~(-3),显著高于夏季(34.0μg·m~(-3)).WSIIs是PM_(2.5)的重要组成,对夏、冬季PM_(2.5)的贡献分别可达52.9%和53.3%.夏、冬季的二次离子(SNA)占WSIIs的比例分别为73.2%和87.6%,其中,SO_4~(2-)和NO~-_3分别是夏、冬季SNA的主导组分,对SNA的贡献分别为37.7%和59.7%.冬季NO~-_3/SO_4~(2-)比值(2.7)显著高于夏季(0.8),体现了移动源(尤其是机动车源)对该季节PM_(2.5)的重要贡献.受来源及气象条件差异的影响,两季节SNA的日变化规律明显.在冬季,随着污染加重,各化学组分及主要气态前体物浓度均显著增加,NO~-_3是引发重污染的关键组分.后向轨迹分析表明,成都两季节气团来向差异明显,夏、冬季聚类对应的WSIIs分别以SO_4~(2-)和NO~-_3为主导,成都周边地区的近距离低空传输对该城市PM_(2.5)污染贡献重大.     

遵义市PM2.5中水溶性离子的污染特征及来源解析

为探究遵义市PM_2.5中水溶性离子的污染特征及来源,于2018年6月~2019年5月采集了遵义市两个采样点共120个PM_2.5样品,并利用离子色谱法对样品中8种水溶性离子进行了分析。结果表明：采样期间,遵义市PM_2.5平均值为47.6±19.3 μg/m³,呈现冬春高、夏秋低的季节变化特征;8种水溶性离子平均质量浓度顺序为SO₄^2- > NO₃^- > NH₄⁺ > Ca²⁺ > K⁺ > Cl^- > Na⁺ > Mg²⁺,平均值为13.74 μg/m³,水溶性离子质量浓度的季节变化与PM_2.5变化趋势相似;SO₄^2-、NO₃^-、NH₄⁺（SNA）是PM_2.5中主要水溶性离子,占比为83.8%,说明遵义市大气PM_2.5二次污染较严重;相关性分析表明,PM_2.5中NH₄⁺主要以（NH₄）₂SO₄、NH₄HSO₄的形式存在,部分以NH₄NO₃的形式存在;[NO₃^-]/[SO₄^2-]小于1,表明固定源为主要污染源;主成分分析结果表明,PM_2.5中水溶性离子主要来源于燃煤、交通混合源、土壤、建筑扬尘及农业源。     

济南市夏、冬季PM2.5中化学组分的季节变化特征及来源解析

为探究济南市大气气溶胶中化学组分的季节变化特征,于2015年夏季、冬季分别连续进行1个月的PM_(2.5)样品采集,并分析无机离子、碳质组分与水溶性二次有机碳(WSOC)的组成、浓度水平及来源.结果表明,济南市冬季PM_(2.5)的质量浓度[(158.3±95.3)μg·m~(-3)]约为夏季[(75.3±25.9)μg·m~(-3)]的2倍,在我国其浓度处于中上等水平.无机离子的总浓度呈夏低冬高的季节变化特征,其中SO_4~(2-)、NO_3~-、NH_4~+是浓度最高的3种离子,且这3种离子的相关性均较好,NH_4~+在夏季和冬季均以(NH_4)_2SO_4和NH_4NO_3的形式存在.大气中存在较高程度的SO_2和NO_2的二次氧化,其中硫氧化率(SOR)呈夏高冬低的变化特征,而氮氧化率(NOR)呈相反的季节变化特征.通过分析PM_(2.5)中阴、阳离子电荷平衡可知,PM_(2.5)呈弱碱性.基于热力学模型ISORROPIA-Ⅱ,结果表明冬季PM_(2.5)的酸性比夏季强.OC与EC浓度均呈夏低冬高的变化特征,由OC/EC的比值、WSOC/OC的比值和估算的二次有机碳(SOC)的浓度可知,夏季二次污染的程度比冬季更为严重.主成分分析(PCA)结果表明,济南市夏季无机离子主要来自二次氧化及生物质燃烧,而冬季无机离子主要来自煤炭燃烧及其产生的前体物经光化学氧化形成的二次污染物.     

Comparative toxicity of silver nanoparticles and silver ions to Escherichia coli

With the increase in silver(Ag)-based products in our lives, it is essential to test the potential toxicity of silver nanoparticles(Ag NPs) and silver ions(Ag ions) on living organisms under various conditions. Here, we investigated the toxicity of Ag NPs with Ag ions to Escherichia coli K-12 strain under various conditions. We observed that both Ag NPs and Ag ions display antibacterial activities, and that Ag ions had higher toxicity to E. coli K-12 strain than Ag NPs under the same concentrations. To understand the toxicity of Ag NPs at a cellular level, reactive oxygen species(ROS) enzymes were detected for use as antioxidant enzymatic biomarkers. We have also studied the toxicity of Ag NPs and Ag ions under various coexistence conditions including: fixed total concentration, with a varied the ratio of Ag NPs to Ag ions; fixed the Ag NPs concentration and then increased the Ag ions concentration; fixed Ag ions concentration and then increasing the Ag NPs concentration.Exposure to Ag NPs and Ag ions clearly had synergistic toxicity; however, decreased toxicity(for a fixed Ag NPs concentration of 5 mg/L, after increasing the Ag ions concentration) to E. coli K-12 strain. Ag NPs and Ag ions in the presence of L-cysteine accelerated the bacterial cell growth rate, thereby reducing the bioavailability of Ag ions released from Ag NPs under the single and coexistence conditions. Further works are needed to consider this potential for Ag NPs and Ag ions toxicity across a range of environmental conditions.Environmental Significance Statement: As silver nanoparticles(Ag NPs)-based products are being broadly used in commercial industries, an ecotoxicological understanding of the Ag NPs being released into the environment should be further considered. Here, we investigate the comparative toxicity of Ag NPs and silver ions(Ag ions) to Escherichia coli K-12 strain, a representative ecotoxicological bioreporter. This study showed that toxicities of Ag NPs and Ag ions to E. coli K-12 strain display different relationships when existing individually or when coexisting, and in the presence of L-cysteine materials. These findings suggest that the toxicology research of nanomaterials should consider conditions when NPs coexist with and without their bioavailable ions.