Efficient removal of Cr(VI) by a 3D Z-scheme TiO2-ZnxCd1-xS graphene aerogel via synergy of adsorption and photocatalysis under visible light

Efficient and robust photocatalysts for environmental pollutants removal with outstanding stability have great significance. Herein, we report a kind of three dimensional (3D) photocatalyst presented as Z-scheme heterojunction, which combining TiO 2 and Zn x Cd 1- x S with graphene aerogel to contrast TiO 2 -Zn x Cd 1- x S graphene aerogel (TSGA, x = 0.5) through a moderate hydrothermal process. The as-prepared Z-scheme TSGA was used to remove aqueous Cr(VI) via a synergistic effect of adsorption and visible light photocatalysis. The adsorption equilibrium can be reached about 40 min, then after about 30 min irradiation under visible light (wavelength ( λ) > 420 nm) the removal rate of Cr(VI) almost reached 100%, which is much better than the performance of pristine TiO 2 and Zn 0.5 Cd 0.5 S, as well as TiO 2 graphene aerogel (TGA) and Zn 0.5 Cd 0.5 S graphene aerogel (SGA). The virulent Cr(VI) was reduced to Cr(III) with hypotoxicity after photocatalysis on TSGA, meanwhile the as-synthesized TSGA presented a good stability and reusability. The reduced graphene oxide (rGO) sheets between TiO 2 and Zn 0.5 Cd 0.5 S played a role as charge transfer mediator, promoting the photoinduced electrons transfer and photocatalysis ability of TSGA was enhanced significantly. Hence,such photocatalyst exhibits a potential application on removing heavy metals with high efficiency and stability from polluted aqueous environment.     

洪涝灾害条件下疏散交通生成预测方法

为有助于有关部门更准确预测洪涝灾害受灾民众的疏散量,结合非集计数据和集计数据的优点,提出分区集计数据的概念,设计了受灾区域分区方法,并通过意向偏好(SP)调查法对我国居民在洪涝条件下疏散交通需求数据进行调查。在此基础上,引入BP神经网络建立基于分区集计数据的疏散交通生成预测模型。利用调查数据进行实证分析发现,所设计方法取得了较好的预测效果,鲁棒性较好,平均相对预测误差仅为1.8%,其预测效果明显优于现有的非集计和整集计模型。     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

Exploring Chemical Variables in Ligustrum lucidum Ait. F. Tricolor (Rehd.) Rehd. in Relation to Air Pollutants and Environmental Conditions

Ligustrum lucidum Ait. f. tricolor (Rehd.) Rehd. in relation to atmospheric pollutants in Córdoba city, Argentina. The study area receives regional pollutants and was categorized taking into account traffic level, industrial density, type of industry, location of the sample point in relation to the street corner, treeless condition, and topographic level. Dried weight/fresh weight ratio (DW/FW) and specific leaf area (SLA) were calculated, and concentrations of chlorophylls, carotenoids, total sulfur, soluble proteins, malondialdehyde (MDA), and hydroperoxy conjugated dienes (HPCD) were determined in leaf samples. Sulfur content correlates positively with traffic density and SLA correlates negatively with some combinations of the categorical variables; MDA correlates positively with topographic level and total protein concentration correlates negatively with treeless condition. On the basis of our results, traffic, location of trees, type of industry, situation of a tree with respect to others, and topographic level are the environmental variables to bear in mind when selecting analogous sampling points in a passive monitoring program. An approximation to predict tree injury may be obtained by measuring DW/FW ratio, proteins, pigments, HPCD, and MDA as they are responsible for the major variability of data.     

Biological Activity in a Heavily Organohalogen-Contaminated River Sediment (8 pp)

Background, Aims and Scope Sediments of the Spittelwasser creek are highly polluted with organic compounds and heavy metals due to the discharge of untreated waste waters from the industrial region of Bitterfeld-Wolfen, Germany over the course of more than one century. However, relatively few data have been published about the chloroorganic contamination of the sediment. This paper reports on the content of different (chloro)organic compounds with special emphasis on polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F), and chlorobenzenes. Existing concepts for the remediation of Spittelwasser sediment include the investigation of natural attenuation processes, which largely depend on the presence of an intact microbial food web. In order to gain more insight in terms of biological activity, we analyzed the capacity of sediment microflora to degrade organic matter by measuring the activities of extracellular hydrolytic enzymes involved in the biogeochemical cycling of carbon, nitrogen, phosphorus and sulfur. Furthermore, the detection of physiologically active bacteria in the sediment, particularly of those known for their capability to reductively dehalogenate organochlorine compounds, illustrates the potential for intrinsic bioremediation processes. Methods PCDD/F and chlorobenzenes were analyzed by gas chromatography(GC)/mass spectrometry and GC/flame ionization detection, respectively. The activities of hydrolytic enzymes were determined from freshly sampled sediment layers using 4-methylumbelliferyl (MUF) or 7-amino-4-methylcoumarin-conjugated model compounds and kinetic fluorescence measurements. Physiologically active bacteria from different sediment layers were microscopically visualized by fluorescence in situ hybridization (FISH). Specific bacteria were identified by 16S rRNA gene amplification and sequencing. Results and Discussion The PCDD/F congener profile was dominated by dibenzofurans. In addition, the presence of specific tetra and pentachlorinated dibenzofurans supported the assumption that extensive magnesium production was one possible source for the high contamination. A range of other chloroorganic compounds, including several isomers of chlorobenzenes, hexachlorocyclohexane and 1,1,1-trichloro-2,2-bis (p-chloro-phenyl)ethane (DDT), was present in the sediment. Activities of extracellular hydrolytic enzymes showed a strong decrease in those sediment layers that were characterized by high contents of absorbable organic halogen (AOX), indicating disturbed organic matter decay. Interestingly, an abnormal increase of cellulolytic enzyme activities below the organochlorine-rich layers was observed, possibly caused by residual cellulose from discharges of sulfite pulping wastes. FISH revealed physiologically active bacteria in most sediment layers from the surface down to the depth of about 60 cm, including members of Desulfitobacterium (D.) and Sulfurospirillum. The presence of D. dehalogenans was confirmed by its partial 16S rRNA gene sequence. Conclusions Results of chemical sediment analyses demonstrated high loads of organochlorine compounds, particularly of PCDD/F. Several years after stopping the waste water discharge to Spittelwasser creek, this sediment remains a main source for pollution of the downstream river system by way of the ongoing mobilization of sediment during high floods. As indicated by our enzyme activity measurements, the decomposition potential for organic matter is low in organochlorine-rich sediment layers. In contrast, the comparably higher enzyme activities in less organochlorine-polluted sediment layers as well as the presence of physiologically active bacteria suggest a considerable potential for natural attenuation. Recommendations and Perspectives From our data we strongly recommend to explore the degradative capacity of sediment microorganisms and the limits for in situ activity towards specific sediment pollutants in more detail. This will give a sound basis for the integration of bioremediation approaches into general concepts to reduce the risk that permanently radiates from this highly contaminated sediment. Submission Editor: Dr. Henner Hollert (Henner.Hollert@urz.uniheidelberg.de)     

焦化废水处理过程中固相物质的形成及处置方法评价

焦化废水处理过程中产生的焦油、污泥和结晶杂盐等固相物质,既有资源属性,又有污染特性,但目前缺乏基于能源、经济及环境影响方面的评估.本研究阐述了3类固相物质的形成机制,建立了质量当量计算及处置方法评价模型.以宝武集团韶关钢铁股份有限公司焦化厂（二期）焦化废水处理工程的A/O/H/O（厌氧/好氧/水解/好氧）流化床工艺作为考察对象,利用工程运行参数和水质统计数据进行固相物质的产量推算,结果发现,焦油、物化污泥、生物污泥（含水率为80%）和工业杂盐的产率分别为0.186、5.80、4.24和1.97 kg·m^-3.通过处置方法评价模型明确了焦油焚烧、污泥热解、结晶杂盐分盐提纯后工业应用是最佳处置方案,在60 m³·h^-1废水处理规模的固相物质处置过程中,每年约产生1177 MWh的能源,获得135.0万元的经济效益,排放627.0 t CO₂,表明能源回收、经济效益和环境影响的协同存在.     

东平湖DOM荧光特征及与氢氧同位素指标的相关性

本研究在运用三维荧光光谱示踪平水期东平湖DOM荧光特征的基础上,进一步分析其与氢氧同位素指标的相关性,并在此基础上尝试对大气降水的DOC贡献进行估算,以期为东平湖的污染控制及治理提供依据.结果表明,东平湖平水期DOM主要由类蛋白组分（C1和C4）和类腐殖质组分（C2和C3）构成,以类酪氨酸组分C1为主,C1所占比重将近50%.平水期东平湖DOM同时受陆源、内源及大气降水的影响,但以内源输入为主;外源输入中大气降水补给与大汶河输入所占比例相当;大气降水中的溶解性有机碳输入可能会对东平湖DOM产生不可忽视的影响,需进一步系统研究.氢氧稳定同位素与溶解态有机碳以及类腐殖质荧光之间呈显著相关关系,可以在一定程度上指示DOM的变化趋势.