Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

阿什河底栖动物群落特征及影响因子

阿什河作为松花江的一级支流,全境包含了从Ⅰ类到劣Ⅴ类的各种类型水质。通过对阿什河沿线底栖动物群落构成进行调查,结合典范对应性分析(CCA)对底栖动物群落与环境因子进行相关性分析得出结论,溶解氧和高锰酸盐指数为研究区域内底栖动物群落构成的主要限制因子。为分析不同水质条件下底栖动物种群变化成因和从生物学角度提出阿什河治理方法提供技术依据。     

上海青浦地区大气降水的化学特征

利用上海青浦地区2003—2014年观测的大气降水监测资料,分析该区域12 a以来大气降水的酸化程度、化学组成特征,探讨降水中化学成分的不同来源及相对贡献。结果表明:降水pH年均值为4.43~6.33,酸雨频率为2.6%~86.8%,降水酸化程度大致经历了明显恶化和波动变化2个阶段。降水电导率年均值为1.77~4.01 m S/m,呈下降趋势。降水中各离子雨量加权平均当量浓度顺序为SO_4~(2-)NH_4~+Ca~(2+)NO_3~-Cl~-Na~+Mg~(2+)F-K~+,SO_4~(2-)、NH+4、Ca~(2+)和NO_3~-是降水中的主要离子,占离子总量的83.0%;降水类型由硫酸型向硫酸和硝酸混合型转变。降水离子中的二次组分SO_4~(2-)、NO_3~-和NH_4~+绝大部分来源于人为源,Ca~(2+)、Mg~(2+)和K+主要来自于土壤源和人为源的贡献,Cl~-主要来自海洋源,同时人为源的影响也不可忽视。     

精细化工固定源废气采样时机的选择探讨

以某医药企业某产品生产过程为实例,综合各工段生产步骤、时间、废气产生情况及估算的污染物浓度等因素,绘制各污染因子排放规律图,从而确定各污染因子排放浓度(排放量)最大的1 h为最佳采样时机。     

化工装置停工放空过程中VOCs排放及估算方法

通过分析化工企业典型开、停车过程的排污节点，表明停车过程中大气挥发性有机污染物主要来自装置退料结束后挂壁、底部滞留和内部空间蒸气等滞留在设备内部的残余物料。在总结化工装置停工放空过程中大气挥发性有机物来源的基础上，讨论并提出大气污染排放量估算方法。     

基于GM-SVR的小样本条件下化工设备可靠性预测

为了准确预测化工设备可靠性趋势，针对化工设备失效寿命数据为小样本的情形，基于灰色估计法与支持向量回归机在小样本数据处理中的优势，建立了失效寿命时间服从三参数威布尔分布的化工设备可靠性模型；结合GM（1，1）和SVR对模型进行参数估计，在压缩机可靠性分析中进行了实例应用，对比分析了最小二乘法、灰色估计法和GM-SVR的估计效果。研究结果表明:GM-SVR对威布尔分布参数的估计精度明显优于最小二乘法和灰色估计法，可以有效地应用于化工设备失效数据为小样本时的可靠性预测。     

TiO2气溶胶快速去除空气中的芥子气模拟剂

在自制反应器内,通过喷射法将纳米TiO_2分散成气溶胶形式,用以降解空气中的芥子气模拟剂二乙基硫醚(DES)。利用自行设计的一套气溶胶沉降行为评价装置,对TiO_2气溶胶的释放条件进行了优化。系统研究了相对湿度、反应温度、光强、催化剂加入量、反应物浓度等因素对DES降解速率和彻底矿化速率的影响。结果表明:TiO_2气溶胶对2.80 mg/L(近似芥子气快速致死浓度)DES降解的半衰期由涂覆型TiO_2的34.5 min缩短至8.5 min;TiO_2气溶胶光催化降解DES的最佳相对湿度为35%,此时CO_2最快生成速率可达34.64μg/(L·min),150 min内彻底矿化比例达66.1%;对于1.5 L反应器,催化剂加入量为30 mg时降解DES的量效比达到最佳状态;TiO_2气溶胶光催化适用于含低浓度DES空气的快速净化。     

铁路沿线可燃重气储罐最小隔离区域划分探讨*

为保护铁路线，以某化工厂距离高铁路线最近的丙烯球罐为例，提出丙烯球罐泄漏最小隔离区域划分方法以及2种保护高铁线路方案，利用重气扩散模型和定量风险评价（QRA）软件分别进行丙烯扩散模拟、爆炸模拟，并进行危险与可操作性分析（HAZOP）和保护层分析（LOPA）。结果表明:球罐发生泄漏及火灾爆炸等事故，会给附近铁路线带来严重破坏；丙烯泄漏或球罐因周围其他设备设施或可燃物质着火而温度升高时，保护措施不足；隧道的安全可靠性要高于仅设1道防爆墙，隧道长度需覆盖最小隔离区域的可及范围，在扩散区域内也需设立普通挡墙，在极度危险情况下，需要实施高铁停开等保护措施。     

基于ALOHA软件的某化工厂选址合理性评估

为了研究化工厂选址的合理性,以山东省某化工厂为例,采用ALOHA(有害大气空中定位软件)模拟软件定量确定厂区内环氧乙烷毒气扩散事故的影响区域和敏感点毒气浓度,结合GoogleEarth地图对影响范围进行实地拟合。结果表明化工厂的环氧乙烷储罐泄漏后,毒气扩散到厂区范围之外,毒气浓度在50~500ppm之间的重伤区(ERPG-2)扩散距离可达1.7km,对周围居民的人身安全构成严重威胁;厂区应根据拟合结果重新规划工厂中环氧乙烷储罐的位置;ALOHA软件对化工厂选址评价提供了新的手段。     

Fatty acids composition of Caenorhabditis elegans using accurate mass GCMS-QTOF

The free living nematode Caenorhabditis elegans is a proven model organism for lipid metabolism research. Total lipids of C. elegans were extracted using chloroform and methanol in 2:1 ratio (v/v). Fatty acids composition of the extracted total lipids was converted to their corresponding fatty acids methyl esters (FAMEs) and analyzed by gas chromatography/accurate mass quadrupole time of flight mass spectrometry using both electron ionization and chemical ionization techniques. Twenty-eight fatty acids consisting of 12 to 22 carbon atoms were identified, 65% of them were unsaturated. Fatty acids containing 12 to17 carbons were mostly saturated with stearic acid (18:0) as the major constituent. Several branched-chain fatty acids were identified. Methyl-14-methylhexadecanoate (iso- 17:0) was the major identified branched fatty acid. This is the first report to detect the intact molecular parent ions of the identified fatty acids in C. elegans using chemical ionization compared to electron ionization which produced fragmentations of the FAMEs.