城市生态系统复合下垫面碳通量特征——以上海市奉贤大学城为例

随着城市化进程的加速和城市人口规模的增加,城市已成为最大的碳源,研究城市生态系统对大气二氧化碳的贡献成为碳循环研究的焦点问题之一。基于研究区域内土地利用现状和一年的涡动观测系统观测数据,结合地理信息技术(Arc GIS)和通量计算工具(Eddypro及ART Footprint Tool)以及碳通量足迹模型分析了上海奉贤大学城碳通量足迹特征,基于此探讨不同下垫面类型,包括以草本和木本等透水层为主的下垫面(称为自然系统),以建筑物、道路等不透水层为主的下垫面(称为社会系统)碳通量的变化特征。研究结果表明:1)在不同风向上,碳通量贡献区范围随着大气稳定度的增加而扩大。大气处于稳定条件下,非主风向上的碳通量贡献区范围(最大范围1 100 m)比主风向上的碳通量贡献区范围(最大范围780m)要大;当大气处于不稳定条件下时主风向和非主风向下的碳通量贡献区范围相差不大(最大范围分别为321和351m)。2)不同下垫面其源汇特征不同,以绿色植物为主的自然系统年碳通量均值为–4.1μmol/m~2/s,表现为碳汇;社会系统的年碳通量均值为8.6μmol/m~2/s,表现为碳源。3)自然系统的碳通量日变化具有较明显的季节分异,变化特征大致呈"U"型;社会系统的碳通量日变化没有明显的季节分异,变化特征大致呈"M"型。绿色植物对城市生态系统的大气二氧化碳有降低作用,结合自然和社会系统的碳通量变化特征可以为以后合理规划城市布局,建立低碳城市提供服务。     

Occurrence and distribution of organochlorine pesticides (HCH and DDT) in sediments collected from East China Sea

Sediments used in this study were collected from different depths of eight sites in East China Sea in November 2002. The levels and distribution patterns of the selected organochlorine pesticides (OCPs=p,p'-DDT, o,p'-DDT, p,p'-DDE, p,p'-DDD, alpha-, beta-, gamma- and delta-HCH) in samples were investigated by the technique of sonication extraction followed by the analysis of gas chromatography (GC) coupled with a micro-electron capture detector (muECD). The concentrations of SigmaHCH and SigmaDDT in the surface sediments were in the range of <0.05-1.45 ng/g (mean 0.76 ng/g), <0.06-6.04 ng/g (mean 3.05 ng/g) based on dry weight (dw), respectively. In the vertical distributions, the SigmaHCH and SigmaDDT were in the range of <0.05-2.52 ng/g, <0.06-10.94 ng/g dw, respectively. Residues of OCPs varied significantly with different sampling sites. SigmaDDT in the surface sediments was correlated well with total organic carbon (TOC) content (r2=0.71), while SigmaHCH showed no obvious correlation. The distribution showed that the sediments from the vicinity estuary or near shore had higher TOC contents, and higher OCPs concentrations. The contamination record indicated an extensive use of OCPs in the catchments from Yangtze River in the past might greatly affect the OCP residues.     

东北农牧交错带典型区土地利用变化及其生态效应

东北农牧交错带是典型的生态环境脆弱区，具有敏感性强、退化趋势明显等生态脆弱性特征。本文利用1986、2002年两期TM/ETM影像，揭示了吉林西部16年来土地利用／覆被的时空变化规律，并依据Costanza R．等人对全球生态系统服务价值的评估方法。分析了土地利用变化对区域生态系统服务价值的影响。结果表明：16年来，吉林西部土地利用／覆被发生了剧烈变化。耕地、盐碱地面积大幅度增加，草地、水域和湿地的面积大幅度减少，区域生态系统服务功能出现明显的衰退。生态系统服务价值显著地减少。吉林西部生态系统服务价值由1986年的767．1亿元／a下降到2002年的624．1亿元／a，减少幅度达18．6％，年均递减速率达1．6％。16年来该区域生态环境恶化和以经济利益为目的的土地利用结构调整，给区域生态系统服务功能和可持续发展能力带来巨大负面影响。     

铜陵市资源利用与经济增长关系分析

资源利用与经济增长之间的关系是资源与环境经济学研究的重要领域。以安徽省铜陵市为例,尝试运用物质流分析方法以及最新发展的自回归分布滞后模型（ARDL）对铜陵市1990~2008年资源利用和实际GDP二者关系进行检验和分析。采用了物质流分析指标中直接物质投入来表征区域资源利用量。ARDL边界检验结果表明了资源利用和经济增长间存在稳定的长期均衡关系。进一步通过ARDL-ECM模型分析了区域经济增长对资源利用长期弹性系数和短期弹性系数,并揭示出资源利用对经济增长的短期和长期因果关系。研究结果表明当前资源投入在一定程度上拉动了铜陵经济增长。同时还对铜陵经济增长和资源保护战略提出了政策建议     

成熟小麦种子中残留的α-淀粉酶对小麦馒头品质的影响

α-淀粉酶通过降解淀粉产生发酵糖,并改变淀粉的物化特性,进而影响小麦制品的品质,其影响与α-淀粉酶来源、含量及小麦制品种类有关.成熟小麦籽粒中残留的α-淀粉酶活性存在极大差异,为了解迟熟α-淀粉酶对馒头品质的影响,以重组自交系群体中α-淀粉酶活性极端的小麦品系为材料,比较高低酶活材料制作的馒头品质之间的差异;并依据小麦...     

我国工业场地再利用的环境风险管理困境及对策

我国污染场地再利用的开发活动日渐增多,伴随场地的再利用对环境和人体健康造成不良影响的环境事件也愈加频繁。在土壤污染防治立法、相关的土壤环境质量标准与监管制度都欠缺的情况下,针对污染场地再利用环境风险管理的项目规划的失当、环境风险评估指标的缺失及监督管理制度不健全的障碍,应加强区域环评与单项建设项目环评的协调,落实场地监管主体、层级及各自的监管职责,形成环境损害赔偿机制,从而健全场地环境风险管理制度。     

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.     

Increasing the recovery of heavy metal ions using two microbial fuel cells operating in parallel with no power output

The present study aimed to improve the performance of microbial fuel cells (MFCs) by using an intermittent connection period without power output. Connecting two MFCs in parallel improved the voltage output of both MFCs until the voltage stabilized. Electric energy was accumulated in two MFCs containing heavy metal ions copper, zinc, and cadmium as electron acceptors by connection in parallel for several hours. The system was then switched to discharge mode with single MFCs with a 1000-Ω resistor connected between anode and cathode. This method successfully achieved highly efficient removal of heavy metal ions. Even when the anolyte was run in sequencing batch mode, the insufficient voltage and power needed to recover heavy metals from the cathode of MFCs can be complemented by the developed method. The average removal ratio of heavy metal ions in sequencing batch mode was 67 % after 10 h. When the discharge time was 20 h, the removal ratios of zinc, copper, and cadmium were 91.5, 86.7, and 83.57 %, respectively; the average removal ratio of these ions after 20 h was only 52.1 % for the control group. Therefore, the average removal efficiency of heavy metal ions increased by 1.75 times using the electrons stored from the bacteria under the open-circuit conditions in parallel mode. Electrochemical impedance data showed that the anode had lower solution resistance and polarization resistance in the parallel stage than as a single MFC, and capacitance increased with the length of time in parallel.

     

Toward better understanding and feasibility of controlling greenhouse gas emissions from treatment of industrial wastewater with activated sludge

Wastewater treatment plants (WWTPs) have been recognized as important sources for anthropogenic greenhouse gas (GHG) emission. The objective of the study was to thoroughly investigate a typical industrial WWTP in southern Taiwan in winter and summer which possesses the emission factors close to those reported values, with the analyses of emission factors, mass fluxes, fugacity, lab-scale in situ experiments, and impact assessment. The activated sludge was the important source in winter and summer, and nitrous oxide (N₂O) was the main contributor (e.g., 57 to 91 % of total GHG emission in a unit of kg carbon dioxide-equivalent/kg chemical oxygen demand). Albeit important for the GHGs in the atmosphere, the fractional contribution of the GHG emission to the carbon or nitrogen removal in wastewater treatment was negligible (e.g., less than 1.5 %). In comparison with the sludge concentration or retention time, adjusting the aeration rate was more effective to diminish the GHG emission in the activated sludge without significantly affecting the treated water quality. When the aeration rate in the activated sludge simulation was reduced by 75 %, the mass flux of N₂O could be diminished by up to 53 % (from 9.6 to 4.5 mg/m²-day). The total emission in the WWTP (including carbon dioxide, methane, and N₂O) would decrease by 46 % (from 0.67 to 0.36 kg CO₂-equiv/kg COD). However, the more important benefit of changing the aeration rate was lowering the energy consumption in operation of the WWTP, as the fractional contribution of pumping to the total emission from the WWTP ranged from 46 to 93 % within the range of the aeration rate tested. Under the circumstance in which reducing the burden of climate change is a global campaign, the findings provide insight regarding the GHG emission from treatment of industrial wastewater and the associated impact on the treatment performance and possible mitigation strategies by operational modifications.

     

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.