河口切变锋引起的滩槽泥沙交换效应

切变锋是河口湾锋系中最为常见和最易观察到的锋面类型。本文对长江口的切变锋进行现场观察和滩，槽同步水文资料分析，简述了长江口切变锋发生的部分和基本特性，提出了切变锋引起的滩，槽泥沙呈现螺旋形交换形式等论点。     

村级水资源的管理与利用研究--来自南方四个村的案例分析

农村水资源及其相关设施的改革需要更多了解村和户层次上的现实状况和机制安排。以避免改革扭曲和失败。通过对湖北和广西共四个村的村委会、关键知情人及120个农户的调查。在村和农户两个层次上描述水资源及其相关设施的管理与利用，尤其是在干旱年份村和户所采取的策略。并深入分析了农村村级水资源管理与利用的制度安排与问题。得出的结论是。追求水利管理效率的市场化改革需要考虑干旱的威胁，要有基于风险的制度设计和法律保障。     

新疆石化工业区颗粒物含水量和酸度对二次无机组分形成的影响

大气二次污染物是新疆独山子区大气污染物的重要组成部分,研究大气中二次组分的转化过程对区域大气污染治理有着重要意义.对新疆独山子区2015年9月至2016年7月采集到的样品进行水溶性组分分析.结果表明,水溶性无机离子(TWSIs)表现出与PM_(2.5)一致的季节变化,为冬季(67.86μg·m~(-3))秋季(13.77μg·m~(-3))春季(10.09μg·m~(-3))夏季(4.85μg·m~(-3));冬季二次无机离子(NH~+_4、SO~(2-)_4和NO~-_3)占TWSIs的98%;结合气溶胶热力学模型(E-AIM)探讨独山子区大气颗粒污染物中颗粒相含水量以及颗粒酸碱性;表明独山子区颗粒物呈酸性,年均原位pH为0.81,其中冬季样品的pH(2.93)值最高;颗粒含水季节变化为冬季(331.32μg·m~(-3))秋季(5.91μg·m~(-3))春季(5.46μg·m~(-3))夏季(1.62μg·m~(-3));年均氮氧化率(NOR)和硫氧化率(SOR)分别为0.13和0.47,表明区域污染物存在二次转化;进一步分析表明颗粒相中的硫酸盐质量浓度受到颗粒含水量和颗粒酸碱度的影响较为明显;高的颗粒相含水条件下区域硝酸盐的形成主要以非均相反应为主.     

科技创新投入对环境全要素生产率的影响机制

使用2004~2015年的中国280个地级市的面板数据,对科技创新投入与环境全要素生产率间的非线性关系、内部影响机理和空间异质性进行分析,结果显示：科技创新投入与环境全要素生产率之间呈现倒N型关系,两个拐点的位置分别为7.722（2257.47万元）和9.610（14913.17万元）;在外部资本进入、污染治理、市场规模效应3种影响路径中,科技创新投入影响下的外部资本进入对环境全要素生产率依然存在污染避难所的负向效应,科技创新投入与外部资本间效应为0.1363,外部资本与环境全要素生产率间效应为-0.0065;科技创新投入能够增强企业的污染治理技术并提高环境全要素生产率,三者间前后效应分别为-0.0277和-0.0311;科技创新的投入与高效益增强了市场规模效应,有效促进生产结构的转型进而提高环境全要素生产率,三者间前后效应为0.0186和0.4346.空间异质性中,外部资本进入与溢出效应带来的污染避难所负效应在中部地区显著,在西部和东北部地区不显著,而污染天堂正效应在东部地区存在但不显著;污染创新治理投入的技术正溢出效应在东部和西部地区效应显著,在中部和东北部不显著;科技创新投入与市场需求规模效应在空间区域无差异且显著为正.建议依据科技创新投入的不同影响路径来实施空间差异化策略.     

高效液相色谱测定花粉素中水溶性维生素

本文介绍一种灵敏而准确的高效液相色谱测定花粉素中五种水溶性维生素(即B_1、B_2、B_5、B_6和B_(12))方法。研究了移动相醋酸盐缓冲溶液的浓度和PH对色谱分离效果的影响,水溶性维生素的定量分析是用日立3011-N的填充柱(其内径为4毫米,长250毫米),以含有2.5％乙醇的醋酸钠-醋酸组成的缓冲溶液(PH6)作为移动相,在波长254纳米处进行紫外检测,变异系数和回收率分别为2-5％和91.0-101.5％。     

新疆河流洪水水污染分析

洪水期间的水污染主要来源于面源。其特点是污染物的浓度高、负荷量大。给工业、渔业和人民生活都会带来不同程度的危害。悬浮物是洪水过程中的主要面污染产物，同时还作为载体吸附和携带大量的有机物、无机盐、重金属类和微生物进入水体，致使河流水质下降。在研究洪水灾害的同时。不应忽略洪水期间的水污染问题。     

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.     

Monitoring of Butter and Ghee (Clarified Butter Fat) for Pesticidal Contamination from Cotton Belt of Haryana, India

Butter (45) and ghee (55) samples were collected from rural and urban areas of cotton growing belt of Haryana and analysed for detecting the residues of organochlorine, synthetic pyrethroid and organophosphate insecticides. The estimation was carried out by using multi residue analytical technique employing GC-ECD and GC-NPD systems equipped with capillary columns. Butter samples were comparatively more contaminated (97%) than ghee (94%), showing more contamination with organochlorine insecticides from urban samples. About 11% samples of butter showed endosulfan residues above MRL value and 2% samples had residues of synthetic pyrethroids and organophosphates each above their respective MRL values. In ghee, residues of HCH & DDT both and of endosulfan exceeded the MRL values in 5 and 20% samples, respectively. Among organophosphates, only chlorpyriphos was detected with 9% samples showing its residue above MRL value. Irrespective of contamination levels, residues above the MRL values were more in ghee. More extensive study covering other agricultural regions/zones of Haryana has been suggested to know the overall scenario of contamination of milk products.     

固相萃取富集高效液相色谱法测定环境水样中的重金属元素

研究了用固相萃取富集高效液相色谱法测定环境水样中痕量重金属镍、铜、银、铅、镉和汞的方法。环境水样中的镍、铜、银、铅、镉和汞用四 -(对二甲氨基苯基 ) -卟啉 (T4 -DMAPP)柱前衍生 ,用C18固相萃取小柱萃取富集镍、铜、银、铅、镉和汞的T4 -DMAPP络合物 ,富集倍数为 1 0 0倍 ,然后用甲醇和四氢呋喃梯度洗脱为流动相 ,WatersXter raTMRP18( 3 9mm× 1 5 0mm)色谱柱为固定相分离 ,用二极管矩阵检测器检测。镍、铜、银、铅、镉和汞的检测限分别为3、2、4、3、1 5ng/L和 3ng/L。方法相对标准偏差为 1 8%— 3 2 % ,标准回收率为 92 %— 1 0 7%。该方法用于测定环境水样中的痕量重金属 ,结果令人满意     

Dynamic taxation schemes to manage irrigation-induced salinity

This paper uses a dynamic model to explore the issue of irrigation-induced salinity, which puts irrigation at risk in most irrigated areas throughout the world. We address the design of instruments that an irrigation district board could implement to induce irrigators to take sustainable irrigation decisions. In our approach, the irrigators located above an aquifer participate in the accumulation of groundwater, a stock pollution. We analyse input-based instruments to induce the agents to follow the optimal stock path.