生活垃圾焚烧废气中二噁英监测采样研究

对当前国内生活垃圾焚烧废气中二噁英监测相关标准和技术规范的解读和实践操作后,认识到生活垃圾焚烧废气中二噁英监测采样的现状及不足.针对不同类型烟道,不同采样时间和不同采样时间间隔分别进行了研究.实验结果表明,同一稳定气流的排放源中,水平烟道和垂直烟道测试二噁英毒性当量排放浓度水平相当;满足监测采样量要求下,不同采样时间和采样时间间隔对二噁英监测结果并无显著差异,对此给出几点相应采样建议.     

利用PUF被动采样技术监测珠江三角洲地区大气中多氯联苯分布

研究了珠江三角洲地区大气中多氯联苯的含量与分布.利用大气被动采样装置,共设立了包括香港在内的珠江三角洲21个大气被动采样点,样品采样时间为2005-08-15～2005-10-14.结果表明,珠三角内地的佛山(2000pg·m-3)是PCBs的高污染地区,内地采样点PCBs含量范围260～2000pg·m-3,平均值670pg·m-3.香港PCBs含量范围170～470pg·m-3,平均值300pg·m-3.香港每个采样点的PCBs含量都接近平均值,含量比较低.珠三角大气中PCBs含量与世界其它地区相比属中度污染区域.结果也表明,PUF大气被动采样器可很好地运用于区域大气PCBs污染分布与特征的对比研究.     

Impacts of converting from leaded to unleaded gasoline on ambient lead concentrations in Jakarta metropolitan area

Total suspended particulate mater （TSP） concentrations were monitored for one year from July 2000 and for one year from April 2003 in Jakarta City. Thirteen elemental TSP components, aluminum （Al）, sodium （Na）, iron （Fe）, lead （Pb）, potassium （K）, zinc （Zn）, titanium （Ti）, manganese （Mn）, bromine （Br）, copper （Cu）, chromium （Cr）, nickel （Ni）, and vanadium （V） were analyzed by a sequential X-ray fluorescence spectrometer. Al, Na, Fe, K, and Pb were major components at most of the sampling locations in 2000. However, only Pb in 2003 dramatically decreased to one tenth. The phase-out of leaded gasoline began on July 1, 2001 in Jakarta City and lead content in gasoline decreased to one tenth, too. The decrease in Pb concentration was a result of the phase-out of leaded gasoline, as lead emissions mainly are exhaust gas from vehicles.     

被动采样监测珠江三角洲NOx、SO2和O3的空间分布特征

为获得珠江三角洲区域污染特征,采用被动扩散采样技术,在珠江三角洲200 km×200 km网格区域内测量11月两周暴露的NO2、SO2和O3浓度水平,采用克里格空间插值法(Kriging)获取NOx、SO2和O3的空间分布特征,并与源清单和已有模型结果比对.采用反向轨迹方法模拟了O3的输送途径.结果表明,采样期间珠江三...     

临安区域大气本底站CO_2浓度特征及其碳源汇变化研究

通过分析2006年8月～2009年7月临安区域大气本底站Flask瓶采样获得的CO2浓度特征,结合碳追踪模式的模拟结果,研究了长三角地区碳源汇变化对CO2浓度的影响.结果表明,临安区域大气本底站的CO2浓度分布在368.3×10-6～414.8×10-6之间,具有较明显的季节波动变化特征,冬季高、夏季低,浓度年较差接近...     

“十二五”地表水国家环境监测网络吉林省水环境国控监测点位布设的构想

根据国家环境质量监测网络,对“十二五”地表水环境质量监测断面调整工作要求,吉林省在辖区内所有水环境质量监测断面及采样点基础上,完成了松花江流域和辽河流域国控监测断面的优化调整工作。在此过程中,如何从国家尺度上,既能充分反映吉林省水环境质量状况,又能满足国家对地表水环境管理需求进行了深入思考。本文以吉林省为例,在确定国控监测断面及采样点的布设中．提出了自己的构想和建议。     

基于相关性分析的PCBA热力学模型修正

目的精确且高效地对印制电路板组件热力学模型进行修正。方法采用基于拉丁超立方抽样试验设计和Speraman等级相关系数计算公式的相关性分析方法,找出电子产品热仿真试验中对元器件表面温度值影响较大的输入参数,然后进一步分析得出输入与输出之间的函数关系。在此基础上给出印制电路板组件(PCBA)热力学模型修正的一般方法流程。最后利用该方法对某航空电子产品中一块PCBA的热力学模型进行修正。结果修正结果较精确且只调用2次有限元软件。结论该热模型修正方法具有较高的精确性和高效性,可推广用于工程实践。     

A soil sampling intercomparison exercise for the ALMERA network

Soil sampling and analysis for radionuclides after an accidental or routine release is a key factor for the dose calculation to members of the public, and for the establishment of possible countermeasures. The IAEA organized for selected laboratories of the ALMERA (Analytical Laboratories for the Measurement of Environmental Radioactivity) network a Soil Sampling Intercomparison Exercise (IAEA/SIE/01) with the objective of comparing soil sampling procedures used by different laboratories. The ALMERA network is a world-wide network of analytical laboratories located in IAEA member states capable of providing reliable and timely analysis of environmental samples in the event of an accidental or intentional release of radioactivity. Ten ALMERA laboratories were selected to participate in the sampling exercise. The soil sampling intercomparison exercise took place in November 2005 in an agricultural area qualified as a “reference site”, aimed at assessing the uncertainties associated with soil sampling in agricultural, semi-natural, urban and contaminated environments and suitable for performing sampling intercomparison. In this paper, the laboratories sampling performance were evaluated.     

Estimation of sampling uncertainty for determination of pesticide residues in plant commodities

In order to provide residue data for refining the estimated sampling uncertainty, a coordinated research program was initiated for performing field studies on residues in individual items of leafy vegetables, small and large crops. The trials were carried out in 13 countries with 3 small fruits, 5 large crops, 2 medium/large crops and 3 leafy vegetables. The 25 pesticide active ingredients applied represented the dicarboximide (3), organophosphorus (8), synthetic pyrethroids (5), phthalimides (2), organochlorine (1) and other types of pesticides (6). In addition, 11 supervised field trials were performed in grapes and lettuce by the pesticide manufacturers, and their results were provided for evaluation. The studies represented actual agriculture practice around the world, and provide reliable data for estimation of sampling uncertainty. Based on the 12346 residue data, the best estimate for the relative sampling uncertainty for composite samples, assuming sample size of 10 for small crops and leafy vegetables and 5 for large crops, with 95% confidence limits in brackets are: small commodities: 0.25 (0.20–0.29); Brassica leafy vegetables: 0.20 (0.16–0.24); large commodities: 0.33 (0.29–0.38).     

Characterization of distribution of pesticide residues in crop units

The characteristic features of distribution of pesticide residues in crop units and single sample increments were studied based on more than 19,000 residue concentrations measured in root vegetables, leafy vegetables, small-, medium- and large-size fruits representing 20 different crops and 46 pesticides. Log-normal, gamma and Weibull distributions were found to provide the best fit for the relative frequency distributions of individual residue data sets. The overall best fit was provided by lognormal distribution. The relative standard deviation of residues (CV) in various crops ranged from 15–170%. The 100–120 residue values being in one data set was too small to identify potential effects of various factors such as the chemical and physical properties of pesticides and the nature of crops. Therefore, the average of CV values, obtained from individual data sets, were calculated and considered to be the best estimate for the likely variability of unit crop residues for treated field (CV = 0.8) and market samples (CV = 1.1), respectively. The larger variation of residues in market samples was attributed to the potential mixing of lots and varying proportion of non-detects. The expectable average variability of residues in composited samples can be calculated from the typical values taking into account the sample size.