水体及沉积物氮磷水平对附植藻类的影响

为了探讨湖泊富营养化过程中沉积物及水体氮、磷浓度对附植藻类的影响,通过室内模拟实验,研究了水体及沉积物氮、磷升高对苦草(Vallisnerianatans(Lour.) Hara)上附植藻类生长、群落组成及其体内氮、磷含量的影响.结果表明,在实验条件下,随着水中氮、磷含量升高,附植藻类生物量及附植藻类氮、磷含量均呈极显著增加(p0.01).随着水体可获得的氮、磷浓度升高,附植藻类的相对丰度有所变化,舟形藻(Navicula)、小球藻(Chlorella)及微囊藻(Microcystis)相对丰度随着氮、磷水平的升高而下降,直链藻(Melosira)则相反,但舟形藻、直链藻、微囊藻、小环藻(Cyclotella)和小球藻均为群落的优势属种.沉积物氮、磷含量升高对附植藻类生物量、优势种丰度及群落氮、磷含量影响较小,均未达到显著水平(p0.05).在实验条件下,沉积物氮、磷含量对附植藻类影响不大,而水体氮、磷浓度升高显著地促进了附植藻类生长.研究结果也为解释富营养化湖泊沉水植物衰退及消亡提供了一定的科学依据.     

海州湾表层沉积物磷的吸附容量及潜在释放风险

利用磷吸附指数（PSI）、磷吸附饱和度（DPS）和磷释放风险指数（ERI）研究了2016年10月和2017年5月海州湾表层沉积物的磷吸附容量及潜在释放风险.结果显示,2016年秋季PSI变化范围为99.58~199.39[mgP/（100g）]/[μmol/L],DPS变化范围为23.118%~34.289%;2017年夏季PSI变化范围是130.29~198.57[mgP/（100g）]/[μmol/L],DPS变化范围为25.545%~42.135%,两次调查中PSI和DPS均表现出相反的平面分布趋势.PSI和Al_ox、Fe_ox呈显著正相关,说明Fe_ox和Al_ox是影响海州湾表层沉积物吸附磷的主要因素,且Fe_ox占主导作用;DPS与Al_ox和Fe_ox分别表现出了显著负相关性和极显著负相关性,说明Al_ox和Fe_ox含量的增大会降低表层沉积物的磷吸附饱和度.2016年10月磷释放风险指数（ERI）的变化范围为11.59%~34.18%,2017年5月磷释放风险指数（ERI）的变化范围为12.86%~32.34%,从2次调查结果整体来看,海州湾表层沉积物的磷释放风险为中度风险.     

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.     

一种硫离子响应杀菌材料的制备及应用研究

目的制备硫离子响应杀菌材料,研究它在不同浓度铜离子条件下的封装效果,以及在不同浓度硫离子条件下的释放行为。方法选择埃洛石纳米管为载体并用透射电镜进行表征、采用真空负载的方式,将甲硝唑和苯骈三氮唑(BTA)分步填充到埃洛石纳米管内部,使用紫外分光光度法测试不同浓度铜离子对纳米管中甲硝唑的封堵效果,以及材料在不同浓度硫离子条件下的释放情况。结果实验中通过调整铜离子浓度发现,铜离子浓度越大对甲硝唑的封堵效果越好,当铜离子浓度达到160 mmol/L时,对甲硝唑的封堵效果最好。杀菌材料在外界硫离子达到0.1mmol/L后,甲硝唑的释放浓度迅速提高,进一步提高硫离子浓度,释放情况改变不明显。结论铜离子浓度达到160 mmol/L时,材料的封装效果是最好的。材料能够对硫离子实现响应释放,且对应的临界浓度为0.1 mmol/L。     

在线自动监测仪与实验室国标方法测定地表水中总氮的比对分析

随着监测技术的发展,地表水中越来越多的污染物可采用自动监测仪器实现实时监测。采用自动监测与实验室国标方法测定地表水中总氮的结果是否可比以及造成差异的原因,文章根据实际样品比对实验对此进行了探讨。     

水中氮类污染物的联系与区别

叙述了水体中含氮类污染物的来源，种类，相互转化方式和条件，表征意义等，以便对水中氮类污染物有一个全面，系统的认识。     

氨氮测定过程中有关问题的探讨

针对纳氏比色法测定氨氮过程中存在的问题，提出了相应的解决办法。消除了混浊和泡沫干扰问题，降低了空白吸光度，提高了测定的准确度。     

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.     

同时分解法测定水中总氮和总磷

通过实验找到了同时测定水中总氮和总磷的条件,实现了总氮和总磷的同时测定。     

Nitrogen isotope ratios in pine bark as an indicator of N emissions from anthropogenic sources

The article describes the use of Scots pine bark to identifynitrogen sources in eastern Germany, as well as background areas in Russia and Bulgaria, by using natural isotope ratios of total nitrogen (N_t) and individual N compoundssuch as ammonium (NH₄ ⁺), nitrate (NO₃ ^-)and amid nitrogen (amide-N). The samples collected were analysed using an elemental analyser in connection with a gas isotope mass spectrometer (EA-IRMS). Natural ¹⁵N abundances in pine bark from impact areas suggest that the ammonium accumulated on the surface of the bark is releasedfrom livestock management. Bark of Scots pines growing near agricultural land had highly depleted ¹⁵N_t values (between –8 and –12), while bark from background areas (unpolluted areas) displayed slightly negative ¹⁵N_t values (mean ¹⁵N_t = –3.8). It is assumed that part of the N adsorbed on the bark surface is mainly derived from ammonia(mean ¹⁵N_t = –40.3) escaping from livestock housing and during the application of manure. This assumption is confirmed by experiments under controlled conditions in which manure samples were spread on soil. In addition, temporal and spatial variations of ¹⁵N_t abundances in pine bark from various locations in eastern Germany as wellas pine stands in Nature Park Dübener Heath are discussed.