浅析被动式FTIR走航监测技术在废气监测中的应用

与传统的监测技术相比,走航监测技术具有监测范围大、响应快速、机动性强的特点,在工业园区及企业废气排放的监测监控、溯源定位方面应用较为广泛。被动式傅里叶红外(FTIR)走航监测技术可以动态、立体、实时地扫描周围一定空域气体中的污染物质。采用被动式FTIR走航监测车,对不同的工业园区企业进行监测,结果表明,被动式FTIR走航监测可对实时发现的超标或异常有毒有害、易燃易爆、异味因子排放装置进行及时的定位和溯源,为环保部门和企业对污染物进行进一步精细化管控提供了有力的科学依据。     

4种方法测定固定污染源废气中的二氧化硫

二氧化硫是我国重点管控的废气污染物。固定污染源废气二氧化硫常用的测定方法有定电位电解法、非分散红外吸收法、便携式紫外吸收法、便携式傅里叶红外法等。测试结果表明:定电位电解法无需预热、响应速度快,但对水汽抗干扰能力较弱;非分散红外吸收法预热时间长,对水汽抗干扰能力较强;便携式紫外吸收法与便携式傅里叶红外法对水汽抗干扰能力强,预热时间短。     

甲烷对非分散红外吸收法测定固定污染源废气中二氧化硫的干扰

二氧化硫是中国重点管控的废气污染物。固定污染源废气二氧化硫测定有定电位电解法、非分散红外吸收法、碘量法等，其中非分散红外吸收法具有选择性好、寿命长、灵敏度高等优势，是固定污染源废气二氧化硫测定的常见方法。在波长为7.3 μm附近的红外吸收波段，甲烷和二氧化硫的特征吸收峰有明显重叠，造成交叉干扰。实验研究表明：甲烷将对二氧化硫的测定引入5%左右的正干扰。现场验证表明：对于含有高浓度甲烷的焦化废气，非分散红外吸收法测定二氧化硫结果显著偏高，经过数学修正后与其他方法可比。在应用非分散红外吸收法测定二氧化硫时，应确认烟气中不含甲烷或其浓度很低，否则应当采取有效手段消除或减小甲烷的干扰。     

原子吸收光谱法测定废气中的镉和铅

用硝酸和过氧化氢进行消解，用火焰原子吸收光谱法测定了废气固定源中的镉和铅，用石墨炉原子吸心光谱法测定了废气散源中的镉和铅。     

石墨炉原子吸收法在环境样品测定中的应用

评述了石墨炉原子吸收光谱法在环境样品测定中的应用 ,内容包括装置与技术、水与废水、土壤与底质 ,以及大气与废气     

工业温室气体CO2排放监测技术前期研究

介绍了国内外二氧化碳（ CO2）气体检测方法，选取红外传感器、非分散红外和气相色谱3种方法监测工业燃煤废气中的CO2。试验结果表明，3种方法的精密度和准确度均满足要求；单一燃煤废气中CO2的体积分数范围为6．70％～15．10％，同一排气筒中CO2体积分数5 min的波动范围为0～22．4％；同一排气筒（单一燃煤废气）中CO2和O2的体积分数有一定的关联性，二者之和基本稳定在19％～21％范围；非分散红外法和气相色谱法测定同一样品的相对偏差为0．9％～3．4％；红外传感器适用于有组织排放的现场监测，另2种方法适用于无组织废气和环境空气监测。     

二氧化硫光谱检测技术

介绍了二氧化硫的性质,综述了紫外荧光分析检测技术、长程吸收光谱检测技术、激光红外吸收检测技术和遥感傅里叶变换红外光谱检测技术的原理及在二氧化硫检测中的应用。     

基于不同原理的低浓度二氧化硫分析仪性能研究

为掌握非色散红外吸收光谱法(NDIR)、紫外差分吸收光谱法(DOAS)、非色散紫外吸收光谱法(NDUV)、红外激光吸收光谱法(OFCEAS)测定烟气中低浓度二氧化硫的差异,通过方法比对实验,对基于不同原理二氧化硫分析仪的响应时间、重复性、线性误差、干扰因素等进行研究。结果表明,不同原理的低浓度二氧化硫分析仪在响应时间、线性误差、重复性、漂移等基本性能上均能满足《HJ/T 76—2007》的要求,但不同原理之间性能指标差异也很明显。不同露点温度对基于NDIR和NDUV法的二氧化硫分析仪有正干扰,CO不干扰二氧化硫的测定,CH4对基于NDIR和OFCEAS法的二氧化硫分析仪有正干扰,NH3和HCl对基于DOAS法的二氧化硫分析仪有正干扰。     

气相色谱／傅立叶变换红外光谱（GC／FT—IR）系统分析有机污染物

1 概述气相色谱(GC)可卓有成效地分离测定环境样品中复杂、痕量、多组分的有机污染物,而傅立时变换红外光谱能提供完整的分子结构信息,因而GC/FTIR系统兼两者之长,适于分离、鉴定多组分的混合物,GC/FTIR系统具有如下几个特点:     

便携式傅里叶红外分析仪测定水中挥发性有机污染物

发明了一种利用GASMET FTIR Dx4020便携式傅里叶红外分析仪测定水中挥发性有机物的实用方法,该方法可以定性定量25个水中挥发性有机物.该方法具有简便快捷、灵敏度高等特点,以苯系物中各单组分物质为例,方法检测限为11 ～45μg/L,相对标准偏差为3.4％ ～ 6.6％,加标回收率为98％ ～ 109％,在浓度范围内具有良好的线性.但对苯系物混合物定量准确性还需进一步研究提高.     

食品生产企业周边空气中的异味监测

简述了食品生产企业产生的异味无机物、有机物的种类,指出其异味主要来源于食品加工、原料贮存以及废物处理等环节,分析了异味物质的产生机理。介绍了空气中异味物质的监测方法包括,嗅辨法、气相色谱-质谱联用法、傅里叶红外光谱法以及气相色谱/气相色谱-质谱与嗅闻联用法等,及其适用性,以及测定结果的评价方法。     

Rapid analysis of tile industry gaseous emissions by ion mobility spectrometry and comparison with solid phase micro-extraction/gas chromatography/mass spectrometry

The present paper reports on a rapid method for the analysis of gaseous emissions from ceramic industry, based on ion mobility spectrometry (IMS) as a means for on-site monitoring of volatile organic compounds (VOCs) produced during tile baking. IMS was calibrated with a set of reference compounds (i.e. ethyl acetate, ethanol, ethylene glycol, diethylene glycol, acetaldehyde, formaldehyde, 2-methyl-1,3-dioxolane, 2,2-dimethyl-1,3-dioxolane, 1,3-dioxolane, 1,4-dioxane, benzene, toluene, cyclohexane, acetone, acetic acid) via air-flow permeation. The technique was tested on a laboratory-scale kiln and tiles prepared with selected glycol- and resin-based additives. Finally, the analytical method was applied to emissions from two industries in the Modena (Italy) ceramic area. The results of all experimental phases were compared to those obtained by solid phase micro-extraction/gas chromatography/mass spectrometry (SPME/GC/MS). IMS showed potential as a real-time monitoring device for quality assessment in ceramic industry emissions. IMS spectra, SPME/GC/MS data, relationship between additives/baking conditions and produced VOCs and advantages and limitations of both techniques will be discussed.     

温室气体光声光谱检测系统的建立及效果分析

“双碳”目标下,温室气体在线长期稳定监测技术是全面掌握温室气体排放及其环境、气候效应,并预测未来变化趋势的重要保障。为了实时在线监测工业生产现场等环境温室气体浓度及其变化趋势,及时采取相应措施,在分析光声光谱信号产生机理及多组分气体混合监测原理的基础上,根据温室气体的主要成分,分析其吸收光谱特性,基于光声光谱的多组分温室气体的定性和定量监测技术,搭建温室气体光声光谱在线监测实验平台,分析监测器内部噪声和环境温度、湿度等外部影响因素,并通过现场测试,分析试验数据,应用吸附法降低内外部因素的影响。结果表明,对称安装传声器和非共振式光声腔能有效削弱外部噪声对测试结果的影响;空气净化器能降低空气中水蒸气和其他气体对测试结果的影响;低、高浓度混合气体监测结果偏差均小于0.5,与GC测试结果偏差小于10%。应用光声光谱技术的环境温室气体监测技术监测范围宽,选择性好,且监测精度达10^-6,适用于环境温室气体浓度在线监测。     

Concentration, distribution and source apportionment of atmospheric polycyclic aromatic hydrocarbons in the southeast suburb of Beijing, China

Total suspended particle samples and gas phase samples were collected at three representative sampling sites in the southeastern suburb of Beijing from March 2005 to January 2006. The samples were analyzed for 16 US EPA priority PAHs using GC/MS. Concentrations of Sigma PAHs in particle and gas phases were 0.21-1.18 x 10(3) ng m(-3) and 9.5 x 10(2) ng-1.03 x 10(5) ng m(-3), respectively. PAH concentrations displayed seasonal variation in the order of winter>spring>autumn>summer for particle phase, and winter>autumn>summer>spring for gas phase. Partial correlation analysis indicates that PAH concentrations in particle phase are negatively correlated with temperature and positively correlated with air pollution index of SO(2). No significant correlation is observed between gas phase PAHs and the auxiliary parameters. Sources of PAH are identified through principal component analysis, and source contributions are estimated through multiple linear regression. Major sources of atmospheric PAHs in the study area include coal combustion, coke industry, vehicular emission and natural gas combustion.     

The research of air pollution based on spectral features in leaf surface of Ficus microcarpa in Guangzhou, China

Nowadays development of industry and traffic are the main contributor to city air pollution in the city of GuangZhou, China. Conventional methods for investigating atmosphere potentially harmful element pollution based on sampling and chemical analysis are time and labor consuming and relatively expensive. Reflectance spectroscopy within the visible-near-infrared region of vegetation in city has been widely used to predict atmosphere constituents due to its rapidity, convenience and accuracy. The objective of this study was to examine the possibility of using leaves reflectance spectra of vegetation as a rapid method to simultaneously assess pollutant (S, Cd, Cu, Hg, Pb, XCl, XF) in the atmosphere of the Guangzhou area. This article has studied the spectral features of polluted leaf surface of Ficus microcarpa in 1985 and 1998. According to the analysis, comprehensive assessment for the change of atmospheric condition and degrees of pollution were given. This conclusion was confirmed by the monitored data got from chemical analysis. Future study with real remote sensing data and field measurements were strongly recommended.     

Cost-effective monitoring for a soil vapor extraction (SVE) system: a simplified modeling and gas sensor test

In order to establish cost-effective monitoring strategies for soil vapor extraction (SVE), a simplified model for multi-component mass transfer of a complex liquid mixture in porous media and gas sensor are proposed and experimentally evaluated. The basic task for the cost-effective monitoring of SVE is to decide how to predict the performances of venting systems in terms of the contaminant vapor removal rate and the time required to accomplish the clean-up specification. The method includes classifying of individual components of a complex mixture on the basis of gas chromatographic (GC) profile and treating each resulting group as a pseudo-single compound. BTEX components of gasoline were selected for model input and the remainders were divided into 4 groups based on their GC retention times. The model proposed in this study is capable of predicting with accuracy volatilization behaviors of gasoline components in soil and the gas sensor (FIGARO TGS 823) was tested by GC-FID to toluene and TPH-GRO(Total Petroleum Hydrocarbon-Gasoline Range Organics) gas samples. A VOC gas sensor was developed which recognizes TPH-GRO concentrations between 250 and 50 ppm. The developed gas sensor test and proposed model can be used as a valuable tool for the cost-effective monitoring for SVE systems.     

Monitoring of marine mucilage formation in Italian seas investigated by infrared spectroscopy and independent component analysis

The aim of this study is to present and to discuss some characteristics of recalcitrant organic matter mechanism and formation. These aggregates called mucilages that are produced by the degradation reactions of several algae, have been investigated by infrared (FTIR) spectroscopy. FTIR spectra of macroaggregates produced by different algal samples have been daily collected in order to investigate the steps of aggregation. Afterwards, they have been elaborated by means of Independent Component Analysis (ICA). ICA investigation of FTIR spectra showed that the global aggregation process of marine mucilage always consisted of two different phases or independent components (ICs). One IC is related to the first degradation step of algal cells leading to the production of mono and oligosaccharides with aminoacids and oligopeptides. The second IC is related to the polymerization of oligosaccharides with aminoacids and oligopeptides and to their interaction with less polar compounds such as lipids thus producing supramolecular structures. The emerging mechanisms of anomalous size aggregates of organic matter match those of natural organic matter aggregation. The approach we suggest is to use synthetic mucilages which allows to monitor the macroaggregates formation because it can hardly be performed by means of natural marine macroaggregates.     

固定污染源废气监测用挥发性卤代烃标准气体制备不确定度研究

针对固定污染源挥发性卤代烃污染物的排放监测需求,采用称量法制备固定污染源废气监测用挥发性卤代烃标准气体(各组分具有浓度差异),并开展制备不确定度评定研究。通过分析原料试剂纯度、液态/气态组分称量、混合液配比、摩尔质量、气体残留等因素和环节引入的不确定度,确立标准气体制备不确定度评定流程。最终,确定固定污染源废气监测用挥发性卤代烃标准气体制备过程引入的相对扩展不确定度(k=2)小于1%。液态/气态组分称量和上一级中间气浓度引入的不确定度对特性量值不确定度的贡献较大,原料试剂纯度对不确定度的影响较小,摩尔质量和气体残留对不确定度的影响可忽略。     

便携式GC-MS测定气体样品中6种典型VOCs的不确定度评定

以便携式GC-MS测定气体样品中6种典型挥发性有机物(VOCs)组分(苯、1,1,2-三氯乙烷、四氯乙烯、乙苯、间二甲苯和1,3,5-三甲基苯)为例,应用不确定度理论,从检测过程和计算方法的角度分析了影响测量不确定度的各种因素:标准气体定值、标准气体稀释、工作曲线的非线性及重复性测定。对各测量不确定度分量进行计算和评定,同时采用稳健统计方法对测定结果进行准确度评价。结果表明:采用便携式GC-MS测定气体样品中VOCs不确定度的主要影响因素是标准气体定值和工作曲线的非线性,其次是重复性测定,标准气体稀释引入的不确定度较小。当VOCs组分含量为200 nmol/mol浓度水平时,测量扩展不确定度为14～17 nmol/mol,测量相对扩展不确定度为7.1%～9.2%(k=2,置信水平为95%),相对误差为1.5%～4.0%。     

Evaluating Ecological Condition Using Soil Biogeochemical Parameters and Near Infrared Reflectance Spectra

Rapid, repeatable assessment of ecological condition is critical for quantitative ecosystem monitoring. Soils provide a sensitive, integrative indicator for which sampling and analysis techniques are well defined. We evaluated soil properties as indicators of ecological condition (subjectively classified into minimally/moderately/severely degraded based on vegetative, hydrologic and edaphic cues) at 526 sites within Ft. Benning military installation (Georgia, USA). For each sample, we measured 17 biogeochemical parameters, and collected high-resolution diffuse reflectance spectra using visible/near infrared reflectance spectroscopy (VNIRS). VNIR spectra have been related to numerous soil attributes — we examine them here for diagnosing integrated response (i.e., ecological condition). We used ordinal logistic regression (OLR) and classification trees (CT) to discriminate between condition categories using both sets of predictors (biogeochemistry and spectra). Sixteen biogeochemical parameters were significantly different across condition categories; however, multivariate models greatly improved discrimination ([calibration, validation] accuracy of [69%, 66%] and [96%, 73%] for OLT and CT models, respectively). Important predictors included total C, total P, and Mehlich K/Ca/Mg. VNIR spectra further improved discrimination ([calibration, validation] accuracy of [74%, 70%] and [96%, 75%] for OLR and CT models, respectively). While spectra were comparably effective at discriminating minimally degraded sites, they were significantly more effective at discriminating severely degraded sites. Error rates across confounding factors suggest that watershed of origin and landscape position were the only important confounders, likely due to imbalanced sampling. We conclude that multivariate diagnosis improves accuracy, and that VNIR spectroscopy, which yields substantial cost and logistical improvements over conventional analyses, provides an effective tool for rapid condition diagnosis.