离子色谱法测定大气中的甲磺酸

建立了以玻璃纤维膜-KB120气溶胶采样器采集大气中甲磺酸的采样方法;以0.1mmol·l-1H3BO3/0.1mmol·l-1 Na2B4O7为淋洗液的甲磺酸离子色谱分析方法.分析方法的精密度和准确度分别为5%和4%.并实测了青岛冬季大气气溶胶中MSA,SO2-4和NO-3的平均浓度分别为0.10μg·m-3,30.40μg·m -3和17.98μg·m-3.     

海水中二甲基硫测定方法的研究

二甲基硫（ＤＭＳ）是雨水天然酸性的贡献者和影响气候变化的重要痕量气体，本文海水中ＤＭＳ测定方法，即采用冷却预浓缩技术处理海水样品，用带有火焰光度检测器气相色谱仪分析，此方法的精密度为１２％，准确度为１０％，对１００ｍｌ海水样品最小检测量为０．０５４ｎｇ（ＤＭＳ）。同时对色谱柱的选取，实验材料的选择，浓缩柱填充材料的选择，鼓气效率，干燥管，样品解析，样品保存等有关实验步骤进行了讨论。     

衍生化气相色谱法测定黄磷诱发氧化水中苯酚降解产物

利用衍生化气相色谱法对黄磷诱发氧化苯酚降解产物进行了研究，在磷酸存在下，以甲醇作酯类衍行生试剂，分别和ＳＥ－５２和ＨＰ－１７石英毛细管双柱结合ＧＣ－ＭＳ进行分离定性定量，黄磷诱发氧化苯酚降解产物以羧酸为主，在反应温度５０℃时，表现为一级反应特征，速率常数（Ｋ）：０．０７７ｍｉｎ＾－１，半衰期（ｔ１／２）：９．０ｍｉｎ。     

Occurrence of PAHs, PCBs and organochlorine pesticides in the Tonghui River of Beijing, China

Tonghui River, a typical river in Beijing, People's Republic of China, was studied for its water and sediment quality, by determining the levels of 16 polycyclic aromatic hydrocarbons (PAHs), 12 polychlorinated biphenyls (PCBs) and 18 organochlorine pesticides in water and sediment samples. Total PAHs, PCBs and organochlorine pesticides concentrations in water varied from 192.5 to 2651 ng/l, 31.58-344.9 ng/l and 134.9-3788 ng/l, respectively. The total PAHs, PCBs and organochlorine pesticides concentrations in surficial sediments were 127-928 ng/g, 0.78-8.47 ng/g and 1.79-13.98 ng/g dry weight, respectively. The results showed that the concentration of these selected organic pollutants in sediment was higher than those in surface water. It may be due to the fact that organic hydrophobic pollutants tend to stay in the sediments. The PAHs were dominated by 2-, 3-ring components in water samples and by 3- and 4-ring compounds in sediment. For organochlorines, alpha-HCH, delta-HCH, Heptachlor, Endosulfan II, DDT are the major organochlorine pesticides in water while Heptachlor, Dieldrin and DDE composed of 95% of total organochlorine pesticides in sediment. For HCHs (HCHs=alpha-HCH+beta-HCH+gamma-HCH+delta-HCH), the predominance of alpha-HCH of total HCHs were clearly observed in water and sediment. PCB18, PCB31 and PCB52 were predominant in water, on average these compounds collectively accounted for 67% of total PCBs. But in sediment, the predominant compounds were PCB28, PCB31 and PCB153, which accounted for 71% of total PCBs in sediment. The levels of micro pollutants in our study areas were compared with other studies.     

电解氧化法处理黄金冶炼厂选冶废水

采用电解氧化法处理同时含有高浓度氰化物与COD的黄金冶炼厂选冶废水,主要考查了外加电压、溶液pH、电解时间及极板间距等因素对氰化物和COD去除率的影响,充分利用原水中Cl^-的阳极氧化效应,深入探讨了电化学氧化过程及污染物氧化去除机理。结果表明,随着外加电压和电解时间的增大,氰化物和COD去除率逐渐增大。以石墨为阳极,钛合金为阴极,采用一阴两阳电解体系对废水进行氧化,当电压为4.5 V、初始pH为7、电解时间为3 h、极板间距为1.5 cm的条件下,总氰化物 (CN_T) 、COD、SCN^-及Cu的去除率最高可达99.6%、96%、99.9%与99.2%。电解过程中体系pH逐渐降低,电场作用下定向迁移至阳极附近的污染物去除主要归因于HClO及ClO^-的间接氧化作用,当pH大于5.0时以HClO的氧化为主,ClO^-的氧化为辅,而pH小于5.0时则主要是HClO的氧化。电解氧化过程中约有36.17%的Cu与SCN^-形成CuSCN沉淀而被除去,剩余的铜则扩散至阴极沉积析出。GC-MS分析表明,烷烃类、酯类、酮类等大分子的C=C键、酯基、羰基等化学键和官能团被氧化断链成小分子物质,随后继续被氧化为H₂O、CO₂和N₂。     

车内空气中挥发性有机物的研究

采用热脱附-气相色谱/质谱联用法对4辆处于静止状态下的车辆内部空气中挥发性有机物（VOCs）进行了研究,共定性检出了48种有机物,其中C6～C9之间的组分较多;并对VOCs的总浓度进行了定量,1#～4#车分别为1846、2289、1104和3146 μg/m^3;其中BTEX占VOCs总量的20%～30%;车内VOCs浓度与温度及车辆使用年限密切相关.     

氯化铁和硫酸铁对酸性土壤中有效态镉和铅污染的修复作用

采用浸泡实验法研究了氯化铁和硫酸铁对酸性土壤中有效态镉和铅污染的修复效果,结果表明,氯化铁和硫酸铁均能有效去除土壤有效态镉和铅污染,Fe(Ⅲ)用量为50~100 mmol/kg时,有效态Cd和Pb的去除效果可达70%~96%。氯化铁和硫酸铁能去除土壤中的水溶态、碳酸盐结合态、腐殖酸结合态、铁锰氧化物结合态和强有机结合态Cd和Pb及交换态Cd。氯化铁和硫酸铁对Cd均既有洗脱修复作用又有固定修复作用,且洗脱修复作用的贡献稍大;氯化铁用量较小时(50 mmol/kg)对Pb既有固定修复又有洗脱修复作用,固定修复作用稍大;用量较大时(100 mmol /kg),对Pb只有洗脱修复作用。硫酸铁对Pb的修复作用则以固定修复作用为主,洗脱修复作用很小。     

西安市2013年PM_(2.5)浓度变化分析研究

全面分析2013年西安市13个国控环境空气质量自动监测子站PM2.5监测数据。结果表明:2013年西安市环境空气中PM2.5年均值为105μg/m3,超过《环境空气质量标准》(GB 3095—2012)二级要求(35μg/m3)200.0%,污染较严重;西安市各子站PM2.5月均值总体呈两边高、中间低的"V"型趋势,全市及各子站PM2.5月均值分别为44~206、32~275μg/m3;采暖期(上半年采暖期为1—3月,下半年采暖期为11—12月)、非采暖期(4—10月)PM2.5平均值分别为156、70μg/m3;上、下半年采暖期PM2.5平均值分别为178、124μg/m3;西安市气象风力以微风为主,雨天集中在5—9月,期间PM2.5月均值小于80μg/m3。     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

Integrating purifiers in refinery hydrogen networks: a retrofit case study

Hydrogen is an important utility in the production of clean fuels such as low-sulfur gasoline and diesel. The shortage of hydrogen resource motivates the integrated management of hydrogen networks with purifiers. In this paper, we propose a systematic approach for the retrofit design of these hydrogen networks. By establishing a state-space superstructure, all possible placements of existing compressors and purifiers are incorporated in the proposed MINLP model. Total annual cost (TAC) is employed as the optimizing object to understand the trade-offs between operating cost and capital cost. A real refinery system near Shanghai of China city is used to illustrate the applicability of the approach.