牛粪-化肥配施对水稻田氮磷迁移转化的影响

在控制外源N输入量相同的前提下,通过田间小区实验,探讨有机肥与化肥不同施用量（牛粪施用量：5,10,20t/hm²）对稻田田间土-水界面氮磷迁移转化特征的影响.结果表明：控制稻田水中NH₄⁺-N、NO₃^--N、TN和TP输出的最佳时期分别为施肥后的第5,30,7,20d,且TN和TP浓度随时间变化符合单指数衰减方程（0.7444≤R²≤0.9724;1.1×10^-6≤F≤0.0055）.采用牛粪部分代替化肥的施肥方式,在一定范围内能降低稻田退水中TN、TP输出负荷（41.8%、36.0%、64.3%;20.3%、39.1%、48.9%）,还可以降低稻田水中N/P,降低水体富营养化风险.同时,牛粪的施用可提高土壤中脲酶和磷酸酶的含量,促进氮磷向植物可吸收形态转化.综合经济成本和生态效益核算,采用10t有机肥代替无机肥的处理是相对经济环保的施肥方法,该施肥方式下,氮磷年输出负荷分别为17.70,1.26kg/hm².     

Potential of secondary aerosol formation from Chinese gasoline engine exhaust

Light-duty gasoline vehicles have drawn public attention in China due to their significant primary emissions of particulate matter and volatile organic compounds(VOCs). However,little information on secondary aerosol formation from exhaust for Chinese vehicles and fuel conditions is available. In this study, chamber experiments were conducted to quantify the potential of secondary aerosol formation from the exhaust of a port fuel injection gasoline engine. The engine and fuel used are common in the Chinese market, and the fuel satisfies the China V gasoline fuel standard. Substantial secondary aerosol formation was observed during a 4–5 hr simulation, which was estimated to represent more than 10 days of equivalent atmospheric photo-oxidation in Beijing. As a consequence, the extreme case secondary organic aerosol(SOA) production was 426 ± 85 mg/kg-fuel, with high levels of precursors and OH exposure. The low hygroscopicity of the aerosols formed inside the chamber suggests that SOA was the dominant chemical composition. Fourteen percent of SOA measured in the chamber experiments could be explained through the oxidation of speciated single-ring aromatics. Unspeciated precursors, such as intermediate-volatility organic compounds and semi-volatile organic compounds, might be significant for SOA formation from gasoline VOCs. We concluded that reductions of emissions of aerosol precursor gases from vehicles are essential to mediate pollution in China.     

Effect of TiO2 nanoparticle aggregation on marine microalgae Isochrysis galbana

TiO_2 nanoparticles(NPs) could adversely impact aquatic ecosystems. However, the aggregation of these NPs could attenuate this effect. In this work, the biological effects of TiO_2 NPs on a marine microalgae Isochrysis galbana were investigated. The aggregation kinetics of TiO_2 NPs under different conditions was also investigated to determine and understand these effects. Results showed that, though TiO_2 NPs had no obvious impact on the size and reproducibility of algal cells under testing conditions, they caused a negative effect on algal chlorophyll, which led to a reduction in photosynthesis. Furthermore, fast aggregation of TiO_2 NPs occurred under all conditions, especially at the pH close to the p Hzpc. Increasing ionic strength and NP concentration also enhanced the aggregation rate.The aggregation and the following sedimentation of TiO_2 NPs reduced their adverse effects on I. galbana.     

水环境综合整治工程存在的风险因素分析研究

水环境综合整治工程存在的风险因素主要有财务风险、造价预测偏离风险、征地困难风险、施工事故风险以及后期质量可靠性风险等,根据风险因素构建评价解释变量和控制变量模型,结合内部控制和信息披露方法进行风险预测评估,采用分组样本检验方法进行水环境综合整治工程风险因素方法,采用T检验方法进行水环境质量可靠性检验,实现风险有效评估和预防。通过实证分析方法进行模板数据分析,提高对水环境综合整治工程存在的风险因素的定量评估能力。     

爆炸螺栓分离对热防护系统冲击响应分析

目的考察在爆炸螺栓分离过程中,作用在TPS(热防护系统)上的冲击响应是否会导致其破坏,从而影响航天器飞行安全,对过程进行数值仿真分析。方法提出四阶段仿真方案,基于MSC.Dytran软件,采用流固耦合方法对爆炸螺栓分离过程进行动力学仿真;根据单个爆炸螺栓地面分离试验的结构进行建模和分析,获取结构响应曲线;再根据实测的冲击响应数据,对爆炸过程中炸药及JWL方程的参数等设置进行调整,获取准确爆炸螺栓的分析模型;最后应用于对真实结构中TPS(热防护系统)在分离过程里受爆炸螺栓冲击响应的动力学分析与评估。结果通过参数调整,获得的仿真模型与爆炸螺栓地面分离试验实测结果一致,证明了仿真模型的有效性和四阶段仿真方案的可行性。结论提取的真实结构爆炸螺栓分离过程中作用在TPS(热防护结构)上的最大加速度响应曲线,可用于指导TPS(热防护系统)的设计。     

重庆九龙坡城区和城郊大气污染物浓度特征研究

为了解重庆市九龙坡区城区和城郊大气污染物浓度特征,分析了城区和城郊国控空气站点2014年3月~2015年2月典型污染物的浓度变化。结果表明,除O_3表现出夏高冬低的季节变化趋势外,其它污染物均表现出冬高夏低的季节变化趋势。PM_(2.5)污染最严重,城区和郊区日均值超标率分别为31.4%和33.4%。城区和城郊的PM_(2.5)浓度之间没有显著性差异(p0.05),城郊O_3浓度显著高于城区(p0.01),NO_2、SO_2、CO和PM_(10)则是城区显著高于城郊(p0.01)。     

Application of cryofocusing hydride generation and atomic fluorescence detection for dissolved mercury species determination in natural water samples

The concentration levels of mercury (Hg) species in natural water samples are usually low. Consequently, accurate analysis with low detection limits is still a major problem. In this work, a method was applied for the simultaneous direct determination of dissolved mercury species in water samples by on-line hydride generation (HG), cryogenic trapping (CT), gas chromatography (GC) and detection by atomic fluorescence spectrometry (AFS). The suitability of the method for real samples with different organic matter and chloride contents was evaluated by recovery experiments in synthetic and natural spiked water samples. The HG method was compared with other current available methods for mercury analysis with respect to the different fraction of mercury analysed, i.e. 'reactive', 'reducible' or total. HG derivatization and SnCl2 reduction (with and without previous oxidation with BrCl) were applied to synthetic and natural (spiked and non-spiked) water samples. The influence of chloride and dissolved organic matter concentrations was studied. The results suggest that the HG procedure is suitable for the simultaneous determination of Hg2+ and MeHg+ in surface water samples. Inorganic mercury analysed by HG (i.e. reducible) is close to the total inorganic mercury.     

Speciation of arsenic using solid phase extraction cartridges

Various solid phase extraction (SPE) cartridges were investigated for speciation of arsenite [As(III)], arsenate [As(v)], monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). Cartridges containing different types of sorbent materials were tested for arsenic retention and elution characteristics. Alumina cartridges were found to completely retain all the four target arsenic species, and are suitable for removal and preconcentration purposes. For speciation analysis, different arsenic species were separated on the basis of their selective retention on and elution from specific cartridges. DMA was retained on a resin-based strong cation exchange cartridge and eluted with 1.0 M HCl. MMA and As(v) were both retained on a silica-based strong anion exchange cartridge and sequentially eluted with 60 mM acetic acid (for MMA) and 1.0 M HCl [for As(v)]. As(III) was not retained on either cartridge and remained in solution. Arsenic species in solution and those eluted from the cartridges were subsequently quantified by using flow injection with hydride generation atomic fluorescence spectrometry (FI-HGAFS) and hydride generation atomic absorption spectrometry (FI-HGAAS). A detection limit of 0.05 microg L(-1) arsenic in water sample was achieved using HGAFS. An application of the method was demonstrated at a drinking water treatment facility. As(III) and As(v) species were determined in water at various stages of treatment. The method is suitable for routine determination of trace levels of arsenic in drinking water to comply with more stringent environmental regulations.     

Development of an ombrotrophic peat bog (low ash) reference material for the determination of elemental concentrations

Given the increasing interest in using peat bogs as archives of atmospheric metal deposition, the lack of validated sample preparation methods and suitable certified reference materials has hindered not only the quality assurance of the generated analytical data but also the interpretation and comparison of peat core metal profiles from different laboratories in the international community. Reference materials play an important role in the evaluation of the accuracy of analytical results and are essential parts of good laboratory practice. An ombrotrophic peat bog reference material has been developed by 14 laboratories from nine countries in an inter-laboratory comparison between February and October 2002. The material has been characterised for both acid-extractable and total concentrations of a range of elements, including Al, As, Ca, Cd, Cr, Cu, Fe, Hg, Mg, Mn, Na, Ni, P, Pb, Ti, V and Zn. The steps involved in the production of the reference material (i.e. collection and preparation, homogeneity and stability studies, and certification) are described in detail.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.