Effective cleanup of oil contamination on bio-inspired superhydrophobic surface

Environmental Science and Pollution Research - The oil-water separation is a popular issue and the removal of oil from bulk water is also meaningful especially in oil spill incident, which not only...     

Distribution,source, and environmental risk assessment of phthalate esters (PAEs) in water,suspended particulate matter,and sediment of a typical Yangtze River Delta City,China

Phthalates (PAEs) in drinking water sources such as the Yangtze River in developing countries had aroused widespread concern. Here, the water, suspended particulate matter (SPM), and sediment samples were collected from 15 sites in wet and dry seasons in Zhenjiang, for the determination of six PAEs (DMP, DEP, DIBP, DBP, DEHP, and DOP) using the solid-phase extraction (SPE) or ultrasonic extraction coupled with gas chromatography-mass spectrometry (GC-MS). The total concentrations of six PAEs (Σ₆PAEs) spanned a range of 2.65–39.31 μg L^?1 in water, 1.97–34.10 μg g^?1 in SPM, and 0.93–34.70 μg g^?1 in sediment. The partition coefficients (K_d1) of PAEs in water and SPM phase ranged from 0.004 to 3.36 L g^?1 in the wet season and from 0.12 to 2.84 L g^?1 in the dry season. K_d2 of PAEs in water and sediment phase was 0.001–9.75 L g^?1 in the wet season and 0.006–8.05 L g^?1 in the dry season. The dominant PAEs were DIBP, DBP, and DEHP in water and SPM, DIBP, DEHP, and DOP in sediment. The concentration of DBP in water exceeded the China Surface Water Standard. The discharge of domestic sewage and industrial wastewater might be the main potential sources of PAEs. The risk quotient (RQ) method used for the risk assessment revealed that DBP (0.01 < RQ < 1) posed a medium risk, while DIBP and DEHP (RQ > 1) posed a high environmental risk in water, DIBP (RQ > 1) also showed a high risk in sediment.

     

Dechlorination and conversion mechanism of trichlorobenzene as a model compound of chlorine-containing wastes by different base-catalyzed combinations

Environmental Science and Pollution Research - Chlorine-containing organic waste (COWs) is a big threat for the waste incineration because of the dioxin generation and equipment corrosion....     

Probabilistic health risk assessment of nitrosamines in drinking water of Shaoxing,Zhejiang, China

Environmental Science and Pollution Research - Nitrosamines (NAms) are potent genotoxic and carcinogenic but widely detected in drinking water. This study aimed to investigate the occurrence of...     

Control of NOx emissions by air staging in small- and medium-scale biomass pellet boilers

Environmental Science and Pollution Research - The effect of air staging strategies on NOx control was investigated on a 210-kW small-scale biomass boiler (SBB) and a 1.4-MW medium-scale biomass...     

水质凯氏氮标准样品的研究

提出了水质凯氏氮标准样品的研究方法。在研究过程中，完成了样品的配制、均匀性、稳定性等工作。经过多家实验室协作定值，确定了样品的标准值及不确定度，并提供使用。     

四氨基酞菁钯/γ-Al_2O_3可见光催化降解罗丹明B

采用浸渍法制备了四氨基酞菁钯(PdTAPc)/γ-Al_2O_3负载型光催化剂,采用UV-Vis,FTIR,XRD,SEM技术对其进行了表征,并将其用于罗丹明B的可见光催化降解,考察了光催化剂加入量、溶液pH、H_2O_2加入量、反应温度对罗丹明B降解效果的影响,并对光催化剂的稳定性进行了测试。表征结果显示,PdTAPc以圆片状负载于γ-Al_2O_3上,二者的结构均未发生明显变化。实验结果表明:在光催化剂加入量1.6 g/L、溶液pH 7.0、H_2O_2加入量12 mmol/L、反应温度20℃的优化条件下反应120 min,罗丹明B(质量浓度0.05 g/L)的降解率高达95%;光催化剂具有较高的稳定性,使用4次后罗丹明B的降解率仍高于85%。     

我国长江流域中三角区域大气污染物排放特征研究

中三角区域已经是我国第四个国家级城市群,也将成为我国经济增长的"第四极"。在经济发展的同时,更需要以节能减排、资源环境等为重点,以实现经济建设与生态文明"双可持续"的协同发展。本文以二氧化硫、氮氧化物、烟(粉)尘为主要大气污染物,对我国中三角区域大气污染物排放进行了详细的分析,并与京津冀、长三角、珠三角、"三区十群"等进行了多方位比较。结果表明,2013年中三角区域二氧化硫排放量为151.7万t,其中工业二氧化硫排放量为140.1万t;氮氧化物排放量为147.2万t,其中工业氮氧化物排放量为93.6万t;烟(粉)尘排放量为81.8万t,其中工业烟(粉)尘排放量为71.4万t。中三角区域二氧化硫、氮氧化物、烟(粉)尘排放量均位于"四极"的第三。中三角区域二氧化硫、氮氧化物、烟(粉)尘单位GDP排放强度分别为25.03t/亿元、24.29 t/亿元、13.50 t/亿元,分别位于"四极"的第一、第二、第二。同时,本文还从经济发展模式、产业结构调整、煤炭消费方式等方面对我国中三角等经济"四极"提出了相关建议。     

Flowering pattern of biodiesel plant Jatropha in frost- and drought-prone regions of Botswana

Jatropha has gained interest as a potential biodiesel feedstock. Nevertheless, its oil production decreases significantly in frost- and drought-prone regions. In this study, we characterized the flowering pattern of Jatropha in Botswana in the 2014/2015 season. Extensive springtime pruning synchronized Jatropha regrowth in summer and effectively stimulated growth after frost damage. Flowering started in February 2015 and peaked in April and May. Wide variations in flowering frequency were observed among different Jatropha accessions. Trees flowering in February and March produced fruit in May, but most trees only flowered in April and May and did not yield fruit because of cold snaps. These observations suggested that harvesting seed before wintertime is the key to improve Jatropha production in the Botswana climate. This study highlighted the importance of inducing early flowering by developing new agricultural managements. These may include frost cover and sun shades to prevent stress-induced damage, canopy control by pruning, optimization of fertilization practice, and/or introduction of superior Jatropha varieties.     

Comparison of the daily global solar radiation from different data sources in Northwest China climate

The solar radiation data are of high importance to the solar energy systems. Conventional methods to obtain the solar radiation data are from weather stations, solar radiation models, commercial software databases, and field measurements. In the present study, a new daily global solar radiation model is proposed, by combining the quadratic function of sunshine fraction and sine function of the day of the year. The solar radiation model calculated data are then compared with China Meteorological Data Sharing System (CMDSS) data, TRNSYS data, and field-measured data in Northwest China climate. It is found that the newly proposed solar radiation model has better performance than the other nine solar radiation models in the literature. The solar radiation model calculated data fit well with the CMDSS annually average data. The TRNSYS data are a bit larger than the CMDSS annually average data in summer half year and a little smaller than those in winter half year. The solar radiation model and the CMDSS annually average data have the best correlation, whereas the TRNSYS data and the field-measured data have the worst correlation. The solar radiation model calculated data have the best correlation with the other three data sources.