盲样考核浓度预判及稀释方法——以原子荧光法测定水中砷为例

以原子荧光法测定水中砷为例,探讨了实验室盲样考核浓度预判及稀释方法.分析了3个砷盲样考核实例,归纳了“直接稀释法”“系列稀释法”“最大稀释法”3种方法,分别适用于盲样浓度范围窄、适中、宽3种情况,检测人员可根据考核机构给定的浓度范围合理选用.     

基于神经网络的洪水预报研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行洪水预报是可行的.对洪水预报问题,初步建立了基于神经网络的洪水预报系统,给出了应用实例.     

影响上海地区热带气旋频数的预测水平评估

在文献「１」研究的基础上，探讨了热带气旋频数预测水平的评估方法，定义了三个更适合于评估每年热带气旋预测性能的参数，对影响上海地区热带气旋频数的预测水平进行了评估，并与“九五”攻关前的业务预测性进行了比较。     

纸片法快速测定水质总大肠菌群

简要介绍了纸片法测定水质总大肠菌群的操作方法，并通过６个实验室的验证试验，研究了纸片法与现行标准方法（多管法）的一致性。结果表明，２种方法的测定结果基本一致，无显著性差异，但纸片法测定周期比多管法缩短了２ｄ，仅需１ｄ即可。     

Population Growth, Available Resources, and Quality of Life： China＇s Post-Reform Economic Development

Two opposing intellectual traditions and their contem- porary developments regarding the relations among population, available resources, and quality of life as reflected in economic growth are reviewe...     

基于网格划分的宜兴市区滨水区可达性评价

城市滨水区可达性不仅仅是空间距离的阻隔，还包括人们对滨水空间的需求及滨水区自身对公众的吸引力。依据潜能模型的概念，选取人口密度、滨水区综合质量和以土地利用表达的交通作为评价因子，借助ArcGIS93软件，建立了基于网格划分的滨水区可达性计算模型。该模型通过对研究区域进行等距网格划分以及定量计算网格内的人口密度、最小阻力成本和滨水区综合质量来进行滨水区可达性的定量评价。运用该模型评价了宜兴市中心城区居民至各滨水区的可达性水平，发现各滨水区平均可达性水平存在明显差异，最高的是团氿，最低的是蠡河；对每个滨水区而言，可达性从空间上均呈现出以滨水区为中心由内向外递减的趋势。评价结果可为城市滨水区合理建设格局的制定和居民享用滨水区的公平性提供决策参考     

Suspended Particulate Matter Distribution in Rural-Industrial Satna and in Urban-Industrial South Delhi

An air quality sampling program was designed and implemented to collect the baseline concentrations of respirable suspended particulates (RSP = PM₁₀), non-respirable suspended particulates (NRSP) and fine suspended particulates (FSP = PM_2.5). Over a three-week period, a 24-h average concentrations were calculated from the samples collected at an industrial site in Southern Delhi and compared to datasets collected in Satna by Envirotech Limited, Okhla, Delhi in order to establish the characteristic difference in emission patterns. PM_2.5, PM₁₀, and total suspended particulates (TSP) concentrations at Satna were 20.5 ± 6.0, 102.1 ± 41.1, and 387.6 ± 222.4 μg m⁻³ and at Delhi were 126.7 ± 28.6, 268.6 ± 39.1, and 687.7 ± 117.4 μg m⁻³. Values at Delhi were well above the standard limit for 24-h PM_2.5 United States National Ambient Air Quality Standards (USNAAQS; 65 μg m⁻³), while values at Satna were under the standard limit. Results were compared with various worldwide studies. These comparisons suggest an immediate need for the promulgation of new PM_2.5 standards. The position of PM₁₀ in Delhi is drastic and needs an immediate attention. PM₁₀ levels at Delhi were also well above the standard limit for 24-h PM₁₀ National Ambient Air Quality Standards (NAAQS; 150 μg m⁻³), while levels at Satna remained under the standard limit. PM_2.5/PM₁₀ values were also calculated to determine PM_2.5 contribution. At Satna, PM_2.5 contribution to PM₁₀ was only 20% compared to 47% in Delhi. TSP values at Delhi were well above, while TSP values at Satna were under, the standard limit for 24-h TSP NAAQS (500 μg m⁻³). At Satna, the PM₁₀ contribution to TSP was only 26% compared to 39% in Delhi. The correlation between PM₁₀, PM_2.5, and TSP were also calculated in order to gain an insight to their sources. Both in Satna and in Delhi, none of the sources was dominant a varied pattern of emissions was obtained, showing the presence of heterogeneous emission density and that nonrespirable suspended particulate (NRSP) formed the greatest part of the particulate load.     

PSO和SVM混合算法确定太湖入湖河流水质主要影响因子

以影响太湖入湖河流水质的24个因子值为研究对象，将PSO算法与SVM算法相结合。PSO算法用于优化SVM算法的参数c和g，以利于快速、高效地确定c和g的全局最优值；SVM算法基于最优的c和g，分别以24，21，18，15，12，9和6个因子作为特征向量预测水质的污染程度。结果表明，当特征向量为9个影响因子时预测率最高。其参数c=18.56，g=1.35，对应的预测率为：全局预测率92.59%，重度污染水质预测率88.89%，轻度污染水质预测率94.45%。因此，通过PSO和SVM混合算法，可以确定影响太湖入湖河流水质的主要因子，利用这些主要因子对水质进行预测预警，不但可以节省时间，而且可以得到精确的结果。     

北京市土地利用分布与水质响应空间关系研究

以北京市16个水质监测断面和饮用水源地作为研究对象,利用2009—2010年16个水质监测自动站监测数据与2009年北京市土地利用数据,分析不同缓冲区内土地利用与过境水质之间的响应关系。研究结果表明,耕地产生的农业非点源污染对水质影响明显;在大尺度区域增加林地面积比例有利于改善水质;离水源近的农村居民用地对水质产生负面影响;工业建设用地对水质影响在较大尺度上更为显著。     

上海市青浦区PM10污染状况及其与气象要素的关系

利用2006-2010年上海市青浦区PM10和同期地面气象要素的监测资料,定量分析PM10的季节变化规律以及PM10与降雨量、大气湿度和风速之间的关系。分析结果表明:PM10浓度在夏季处于低值,冬季处于高值;5mm/d以上的降雨对PM10有显著的清除作用,且春夏季降雨的清除作用大于秋冬季节。PM10浓度与大气湿度基本呈负相关关系。风速在一定范围内有利于PM10的扩散但不至造成扬尘,春夏季节的适宜风速是1.5～3.5m/s,冬季的适宜风速是1.5～2.5m/s。