Matrix effect on the performance of headspace solid phase microextraction method for the analysis of target volatile organic compounds (VOCs) in environmental samples

Solid phase microextraction (SPME) is a fast, cheap and solvent free methodology widely used for environmental analysis. A SPME methodology has been optimized for the analysis of VOCs in a range of matrices covering different soils of varying textures, organic matrices from manures and composts from different origins, and biochars. The performance of the technique was compared for the different matrices spiked with a multicomponent VOC mixture, selected to cover different VOC groups of environmental relevance (ketone, terpene, alcohol, aliphatic hydrocarbons and alkylbenzenes). VOC recovery was dependent on the nature itself of the VOC and the matrix characteristics. The SPME analysis of non-polar compounds, such as alkylbenzenes, terpenes and aliphatic hydrocarbons, was markedly affected by the type of matrix as a consequence of the competition for the adsorption sites in the SPME fiber. These non-polar compounds were strongly retained in the biochar surfaces limiting the use of SPME for this type of matrices. However, this adsorption capacity was not evident when biochar had undergone a weathering/aging process through composting. Polar compounds (alcohol and ketone) showed a similar behavior in all matrices, as a consequence of the hydrophilic characteristics, affected by water content in the matrix. SPME showed a good performance for soils and organic matrices especially for non-polar compounds, achieving a limit of detection (LD) and limit of quantification (LQ) of 0.02 and 0.03 ng g⁻¹ for non-polar compounds and poor extraction for more hydrophilic and polar compounds (LD and LQ higher 310 and 490 ng g⁻¹). The characteristics of the matrix, especially pH and organic matter, had a marked impact on SPME, due to the competition of the analytes for active sites in the fiber, but VOC biodegradation should not be discarded in matrices with active microbial biomass.     

Development and application of LC–APCI–MS method for biomonitoring of animal and human exposure to imidacloprid

Imidacloprid (IMI) is a relatively new neuro-active neonicotinoid insecticide and nowadays one of the largest selling insecticides worldwide. In the present study a LC–APCI–MS based method was developed and validated for the quantification of imidacloprid and its main metabolite 6-chloronicotinic acid (6-CINA) in urine and hair specimens. The method was tested in biomonitoring of intentionally exposed animals and subsequently applied for biomonitoring of Cretan urban and rural population.     

航空维修人为差错影响因素分析中的模糊层次分析法

航空维修中的人为差错影响因素分析对于预防事故发生至关重要,如何定量分析及辨识出主要影响因素是亟待解决的问题。为此,结合航空维修实际,提出运用群组模糊层次分析法对人为差错影响因素进行量化排序和分类,归纳细化了影响因素层次体系,并给出了分析计算流程。以一起由维修人为差错所引发的航空事故为例进行了实证分析,结果表明:该方法能够辨识出航空维修中人为差错产生的主要影响因素,进而对制订最优事故预防方案,控制和减少该类事故的发生有积极意义。同时该方法对其他行业中的人为差错主要影响因素辨识有着一定的参考价值。     

Monitoring of 943 organic micropollutants in wastewater from municipal wastewater treatment plants with secondary and advanced treatment processes

To perform a systematic survey on the occurrence and removal of micropollutants during municipal wastewater treatment, 943 semi-volatile organic chemicals in 32 wastewater samples including influents of secondary treatments, secondary effluents and final effluents(effluents of advanced treatments), which were collected from seven full-scale municipal wastewater treatment plants(MWTPs) in China, were examined by gas chromatography-mass spectrometry(GC-MS) coupled with an automated identification and quantification system with a database(AIQS-DB). In total, 196 and 145 chemicals were detected in secondary and final effluents, respectively. The majority of the total concentrations(average removal efficiency, 87.0%±5.9%) of the micropollutants were removed during secondary treatments. However, advanced treatments achieved different micropollutant removal extents from secondary effluents depending on the different treatment processes employed. Highly variable removal efficiencies of total concentrations(32.7%–99.3%) were observed among the different advanced processes. Among them,ozonation-based processes could remove 70.0%–80.9% of the total concentrations of studied micropollutants. The potentially harmful micropollutants, based on their detection frequency and concentration in secondary and final effluents, were polycyclic aromatic hydrocarbons(PAHs)(2-methylnaphthalene, fluoranthene, pyrene, naphthalene and phenanthrene), phosphorus flame retardants(tributyl phosphate(TBP), tris(2-chloroethyl)phosphate(TCEP) and tris(1,3-dichloro-2-propyl) phosphate(TDCP)), phthalates(bis(2-ethylhexyl)phthalate(DEHP)), benzothiazoles(benzothiazole,2-(methylthio)-benzothiazol, and 2(3H)-benzothiazolone) and phenol. This study indicated that the presence of considerable amounts of micropollutants in secondary effluent creates the need for suitable advanced treatment before their reuse.     

基于综合评判模型的煤与瓦斯突出危险程度预测

针对某些矿井出现的"低指标"突出现象,提出一种新的综合评判模型,用于煤与瓦斯突出危险程度预测。选取鉴定煤与瓦斯突出的4个单项指标临界值作为参考点,建立煤与瓦斯突出危险性预测综合评判准则。当评判距离Ri属于[0,1],判断突出强度;当Ri属于[-1,0],判断突出危险可能性,量化了突出强度及可能突出的危险程度,并划分突出强度、突出危险可能性等级。利用模型对淮南矿突出程度进行预测,预测结果与实际吻合,实现了定性语言与定量数据相结合的突出预测,为煤与瓦斯突出提供了一种新的预测方法。     

GIS支持下的滑坡空间预测与危险等级划分

以日本早池峰山地域为研究对象,借助航片、TM影像、数字化地图以及各种统计资料和现有专题图等主要数据,利用美国MicroImage公司开发的TNTmips地理信息系统,将数量化理论与地理信息系统紧密结合,对区域滑坡进行了空间预测和危险等级划分.提取并计算了滑坡以及包括坡度、坡向、海拔、坡面倾斜类型、降雨、植被、土壤和地质等8个滑坡影响因子,得到了滑坡及其影响因子的空间分布图.在此基础上,抽取372个样本,应用数量化理论对这些样本进行了分析,建立了区域滑坡预测模型.该模型经研究区域共1296384个样本检验,模型预测精度达80.49%.最后,以滑坡发生概率为基准,对滑坡危险等级进行了划分.     

Spatial Modeling and Habitat Quantification for Two Diadromous Fish in New Zealand Streams: A GIS-Based Approach with Application for Conservation Management

We developed logistic regression models from data on biotic and abiotic variables for 172 sites on Banks Peninsula, New Zealand, to predict the probability of occurrence of two diadromous fish, banded kokopu (Galaxias fasciatus) and koaro (G. brevipinnis). Banded kokopu occurrence was positively associated with small streams and low-intensity land uses (e.g., sheep grazing or forested), whereas intensive land uses (e.g., mixed sheep and cattle farming) and lack of riparian forest cover impacted negatively on occurrence at sampled sites. Also, if forests were positioned predominantly in lowland areas, banded kokopu occurrence declined with increasing distance to stream mouth. Koaro occurrence was positively influenced by catchment forest cover, high stream altitudes, and areas of no farming activity or mixed land uses. Intensive land uses, distance to stream mouth, and presence of banded kokopu negatively influenced koaro occupancy of stream reaches. Banded kokopu and koaro presence was predicted in 86.0% and 83.7% agreement, respectively, with field observations. We used the models to quantify the amount of stream reaches that would be of good, moderate, and poor quality, based on the probability of occurrences of the fish being greater than 0.75, between 0.75 and 0.5, or less than 0.5, respectively. Hindcasting using historical data on vegetation cover undertaken for one catchment, Pigeon Bay, showed they would have occupied most of the waterway before anthropogenic modification. We also modeled potential future scenarios to project potential fish distribution.     

区域环境负荷计量的当量因子法——以广东省佛山市富营养化污染负荷为例

提出了一种基于当量因子的环境负荷计量方法,其原理是根据不同环境干扰因子对同一种环境影响类型的相对贡献大小来进行计量.具体包括环境影响类型的确定、环境干扰因子的识别与归类,以及按当量关系进行汇总3个步骤.该方法可为区域环境规划与污染控制提供技术支持.以广东省佛山市富营养化污染负荷计量为实证进行了研究.结果表明,2001年排放的富营养化污染负荷量约为437.0kt NO₃^-当量,其中贡献最大的是农业生产部门,其次是工业生产部门和人类生活.主要污染物是氨的排放,其次是NO_x的排放.     

大气环流型对珠三角2015～2020年臭氧变化的影响

基于2015～2020年的珠三角臭氧(O₃)日最大8 h浓度平均值[MDA8 O₃,ρ(O₃-8h)]的观测数据和气象再分析数据,运用Lamb-Jenkinson天气分型法(LWTs)分析不同大气环流型的特征并定量其对MDA8 O₃年际变化的贡献.结果表明,珠三角发生的18种天气型中ASW型更容易出现O₃污染现象,而NE型会导致更严重的O₃污染.根据850 hPa风场的风向变化及中心系统的不同位置将18种天气类型合并为5个天气类别来探讨不同天气型的O₃生成机制,发现ρ(O₃-8h)高的天气类别为N-E-S方向类别[(161±68)μg·m^-3]和A类别[(122±39)μg·m^-3],二者ρ(O₃-8h)与日最高气温和太阳净辐射量都呈显著正相关.N-E-S方向类别为秋季主导大气环流型,而A类别多发生在春季,其中春季珠三角发生的90%O_3<...     

矿山地质环境安全评估的Bayes判别分析法及应用

从矿山地质环境的实际特性出发,应用Bayes判别分析方法评估矿山地质环境问题。选用13项影响矿山地质环境的区域地质条件、环境现状、矿产资源开发规划、生态恢复治理状况的具体评估指标,以大量实测资料作为学习样本,并按各总体的样本比例确定先验概率,建立了矿山地质环境评估的Bayes判别分析模型。将该模型用于预测样本的评估中,采用回代估计法对判别结果进行验证,误判率为零,判别结果和实际一致。研究结果表明,Bayes判别分析模型是一种结构稳定、判别准确率很高的评估模型,能够用于矿山地质环境的评估。