两种有机锌络合物处理棉花的霉变及热解行为研究

棉花是纺织业的重要原料,是人民群众生活不可或缺的必需品,同时也是我国进出口重要的商品。研究如何安全有效地进行棉花的储备具有十分重要的现实意义。棉纤维本身含有脂肪、蜡质和果胶等适合微生物生长繁殖的营养物质。在棉花储备中,高的回潮率会加速微生物的繁殖,进而产生热量。热量的累积会引起温度升高以及棉花霉变,不利于棉花的安全有效储存。因此,通过静电吸附法将安全无毒的有机锌络合物附着在棉纤维表面,研究表明,相同条件下,处理棉的霉变状况明显得到抑制。加速发霉条件下,未处理棉的相对于白纸的平均色差值为28.10,而双乙酸锌以及苯甲酸锌防霉处理棉的色差值分别为5.16和5.86,下降了81.6%和79.1%。自然发霉条件下,双乙酸锌以及苯甲酸锌防霉处理棉的色差值分别下降了53.8%和50.7%。同时研究了纯棉以及处理棉氮气下的热分解动力学,相比于未处理棉,双乙酸锌防霉处理后活化能下降了15.8%,而苯甲酸锌防霉处理后活化能下降了10.9%。此外,利用实时红外和热重红外联用技术得到了样品在热解过程中固相以及气相的裂解产物的红外谱图,发现防霉处理能一定程度上抑制棉花热解。     

3种喹诺酮类抗生素在骆马湖饮用水源地沉积物上的吸附特征

为探究喹诺酮类抗生素(QNs)在饮用水源地的吸附特征,2019年6月于江苏骆马湖饮用水源地采集沉积物样品,用5种动力学方程拟合沉积物对诺氟沙星(NOR)、环丙沙星(CIP)和氧氟沙星(OFL)的吸附动力学过程,并用Langmuir和Freundlich模型拟合以上3种QNs的吸附热力学过程,分析了pH、水土比和不同Na~+、Ca~(2+)、Mg~(2+)、Al~(3+)浓度对吸附过程的影响。结果表明:骆马湖沉积物对3种QNs的吸附在8h达到平衡,准二级动力学方程和Langmuir模型能更好地描述3种QNs的吸附动力学和热力学过程。酸性条件下,3种QNs的吸附效果更好,且在pH=5时平衡吸附量最大。平衡吸附量随着水土比的上升而下降。Na~+对吸附的抑制作用不明显,Ca~(2+)和Mg~(2+)的抑制作用较强,而Al~(3+)对吸附表现为促进作用。应在枯水期和南水北调时期加强对骆马湖饮用水源地(尤其是东北部)QNs污染的关注。     

Simultaneous determination of estrogenic odorant alkylphenols,chlorophenols, and their derivatives in water using online headspace solid phase microextraction coupled with gas chromatography-mass spectrometry

A simple online headspace solid-phase microextraction (HS-SPME) coupled with the gas chromatography-mass spectrometry (GC-MS) method was developed for simultaneous determination of trace amounts of nine estrogenic odorant alkylphenols and chlorophenols and their derivatives in water samples. The extraction conditions of HS-SPME were optimized including fiber selection, extraction temperature, extraction time, and salt concentration. Results showed that divinylbenzene/Carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber was the most appropriate one among the three selected commercial fibers, and the optimal extraction temperature, time, and salt concentration were 70 °C, 30 min, and 0.25 g/mL, respectively. The developed method was validated and showed good linearity (R ²?>?0.989), low limit of detection (LOD, 0.002–0.5 μg/L), and excellent recoveries (76–126 %) with low relative standard deviation (RSD, 0.7–12.9 %). The developed method was finally applied to two surface water samples and some of these target compounds were detected. All these detected compounds were below their odor thresholds, except for 2,4,6-TCAS and 2,4,6-TBAS wherein their concentrations were near their odor thresholds. However, in the two surface water samples, these detected compounds contributed to a certain amount of estrogenicity, which seemed to suggest that more attention should be paid to the issue of estrogenicity rather than to the odor problem.     

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.     

Analysis of urban carbon emission efficiency and influencing factors in the Yellow River Basin

Environmental Science and Pollution Research - The Yellow River Basin is an energy-rich area. The low-carbon development of the Yellow River Basin is one of the ways to achieve ecological...     

游泳池中隐孢子虫卵囊替代物的去除

隐孢子虫是常见的球状寄生虫。接触含有隐孢子虫卵囊的水体可致隐孢子虫病,主要的临床表现为腹泻,严重者可致死。游泳池是主要的传播场所。采用容积为5 500 L的游泳池,进行隐孢子虫卵囊替代物的修复模拟实验,研究在游泳池条件下絮凝沙滤技术对直径为4.5μm隐孢子虫卵囊替代物的去除效果。实验结果表明,氯化铝剂量为0.1 mg/L,沙滤料高度为30 cm,内循环流速为11.5 L/min,对隐孢子虫卵囊替代物去除率在90%以上。该技术对浊度具有较好的处理效果,当进水浊度降低且接近于出水浊度时,系统进入稳定运行阶段,对虫卵囊替代物去除率可以达到96%。     

Distribution and ecological risk assessment of organochlorine pesticides in surface sediments from the East Lake,China

Organochlorine pesticides (OCPs) are ubiquitous pollutants, and their presence in urban lakes is a concern for human and ecological health. Surface sediments in the East Lake, China, were collected in winter 2012 and summer 2013 to investigate concentrations, distribution patterns, possible sources, and potential ecological risks of OCPs in this area. The total concentrations of 14 OCPs ranged from 6.3 to 400 ng g^?1 dry weight (dw) with an average concentration of 79 ng g^?1 dw. The mean values of hexachlorocyclohexanes (HCHs) (α-, β-, γ-, and δ-HCH) and dichlorodiphenyltrichloroethanes (DDTs) (p,p’-DDE, p,p’-DDD, and p,p’-DDT) were 36 and 7.6 ng g^?1 dw, accounting for 45 and 10 % of the total OCPs, respectively. The concentrations of OCPs in sediment samples collected in winter were significantly higher than those in summer, especially the HCHs, of which in winter were two times greater than summer. Composition analyses indicated that DDTs and endosulfan were mainly from historical contribution. Historical use of technical HCH and new input of lindane were probably the source of HCHs in the East Lake. Most sampling sites of HCHs and DDTs were found to have the potential ecological risk based on levels specified in the sediment quality standards.     

Spatial and seasonal characteristics of river water chemistry in the Taizi River in Northeast China

Anthropogenic activities have led to water quality deterioration in many parts of the world, especially in Northeast China. The current work investigated the spatiotemporal variations of water quality in the Taizi River by multivariate statistical analysis of data from the 67 sampling sites in the mainstream and major tributaries of the river during dry and rainy seasons. One-way analysis of variance indicated that the 20 measured variables (except pH, 5-day biological oxygen demand, permanganate index, and chloride, orthophosphate, and total phosphorus concentrations) showed significant seasonal (p?≤?0.05) and spatial (p?<?0.05) variations among the mainstream and major tributaries of the river. Hierarchical cluster analysis of data from the different seasons classified the mainstream and tributaries of the river into three clusters, namely, less, moderately, and highly polluted clusters. Factor analysis extracted five factors from data in the different seasons, which accounted for the high percentage of the total variance and reflected the integrated characteristics of water chemistry, organic pollution, phosphorous pollution, denitrification effect, and nitrogen pollution. The results indicate that river pollution in Northeast China was mainly from natural and/or anthropogenic sources, e.g., rainfall, domestic wastewater, agricultural runoff, and industrial discharge.     

Seasonal variation and spatial distribution of atmospheric mercury and its gas-particulate partition in the vicinity of a semiconductor manufacturing complex

This study investigated the tempospatial variation of atmospheric mercury and its gas-particulate partition in the vicinity of a semiconductor manufacturing complex, where a plenty of flat-monitor manufacturing plants using elemental mercury as a light-initiating medium to produce backlight fluorescence tubes and may fugitively emit mercury-containing air pollutants to the atmosphere. Atmospheric mercury speciation, concentration, and the partition of total gaseous mercury (TGM) and particulate mercury (Hg_p) were measured at four sites surrounding the semiconductor manufacturing intensive district/complex. One-year field measurement showed that the seasonal averaged concentrations of TGM and Hg_p were in the range of 3.30–6.89 and 0.06–0.14 ng/m³, respectively, whereas the highest 24-h TGM and Hg_p concentrations were 10.33 and 0.26 ng/m³, respectively. Atmospheric mercury apportioned as 92.59–99.01 % TGM and 0.99–7.41 % Hg_p. As a whole, the highest and lowest concentrations of TGM were observed in the winter and summer sampling periods, respectively, whereas the concentration of Hg_p did not vary much seasonally. The highest TGM concentrations were always observed at the downwind sites, indicating that the semiconductor manufacturing complex was a hot spot of mercury emission source, which caused severe atmospheric mercury contamination over the investigation region.