玉溪市中心城区环境空气TSP源解析

应用受体模式的化学质量平衡法(CMB)对玉溪市中心城区3个监测点的总悬浮颗粒物进行污染源的源解析,得出5类污染源对TSP的平均贡献率为土壤尘40 75%、建材尘31 06%、煤烟尘18 37%、钢铁尘8 09%和交通尘1 73%。     

大气颗粒物源解析受体模型优化技术研究

针对大气颗粒物来源解析技术存在的2大问题:二次有机碳(SOC)对CMB模型的影响及源与受体不匹配程度对源成分谱共线性的影响,给出了解决方案.对于SOC影响的问题,提出从受体的角度扣除SOC,对CMB模型进行修正,降低SOC的影响;对于共线性问题,提出了PCA/MLR-CMB复合模型,复合模型首先进行PCA/MLR的解析,降低受体中未知源的影响,使得纳入CMB模型中的源和受体匹配程度大大提高,从而使得共线性源类能够得到理想的结果.     

基于OP-FTIR在线监测的气态污染物源解析技术

为分析华北地区某化工装置A与企业B对某受体区域污染程度的影响,选用化学质量平衡模型(CMB)定量解析污染来源。首先,利用开路式傅里叶变换红外光谱(OP-FTIR)设备在线监测A与B的污染物排放数据,结合相关性分析建立了二者的成分谱库;在此基础上,根据受体区域污染物在线监测数据,利用CMB完成了污染物来源的定量解析。研究结果表明:受体位置的烷烃及苯系物主要来自A,氨气近乎完全来自B,该解析结果与现场实际及工艺资料相吻合。     

区域源解析模式的建立

运用数值分类学手段建立了一种迭代的(iterative)、分裂的(divisive)、不加权的(non-weighting)、非交迭(nonoverlapping)的运算程式(IDNN模式),从实测原始数据中提取可近似作为纯源的样品和相应的性状,通过分析这些近似纯源样品及与之关系密切的其它特征样品的采样参数和成分特征可鉴别或识别源的类型,运用化学质量平衡法(CMB)求解受体样品的定量来源.在对模拟数据的验证处理中,IDNN模式显示了初步成功.     

来源解析有哪些方法?

<正>类型大气颗粒物来源解析技术方法主要包括源清单法、源模型法和受体模型法,每种技术方法或者方法组合都有特定的适用范围.源清单法:根据排放因子及活动水平估算污染物排放量,据此排放量识别对环境空气中颗粒物有贡献的主要排放源.源模型法:以不同尺度数值模式方法定量描述大气污染物从源到受体所经历的物理化学过程,定量估算不同地     

受体模型在大气颗粒物源解析中的应用和进展

综述用于大气颗粒物研究的受体模型在国内外的发展概况。对受体模型与扩散模型的特点,常用受体模型的技术内容,受体模型在城区尺度、区域尺度和全球尺度颗粒物源解析研究中的应用作了详细阐述。介绍了受体模型研究的一些热点问题,提出当前应开展的主要研究课题。     

大气颗粒物混合尘溯源解析新方法

针对采用化学质量平衡模型(CMB)进行大气颗粒物源解析时,混合尘源由于与单一尘源存在严重共线性而不能代入模型的问题,首先对混合尘进行源解析,根据解析结果将混合尘分拆为对应的单一尘源,然后与原有的单一尘源共同代入CMB进行源解析,提出了大气颗粒物混合尘溯源解析新方法.将混合尘溯源解析新方法应用于某市的大气颗粒物尘源解析,得到扬尘的贡献率为28.75%,较二重源解析技术的46.3%和二重源解析技术改进方法的38.38%都要低,表明新方法更好地解决了共线源代入CMB出现的问题.受体元素计算值与测量值的比值较二重源解析技术改进方法有更多元素接近1,表明新方法建立的模型更加合理.     

天津近海夏季大气颗粒物元素特征及来源解析

2006年夏季在天津近海海域走航采集总悬浮颗粒物(TSP)样品,用于研究近海海域大气颗粒物污染特征和来源。采集到的样品通过重量分析得到颗粒物浓度,同时进行元素分析。并结合气象要素分析,显示陆源污染物在陆风影响下传输到海上,解释了采样期间近海海域TSP浓度较高的现象。研究使用富集因子和化学质量受体平衡(CMB)模型对近海海域TSP进行解析。通过富集因子分析,估计近海海域TSP来源主要有人为源、地壳源和海盐粒子源。CMB结果显示燃煤飞灰所代表的人为源对TSP贡献最大(36.14%),其次是地壳源类(33.26%)以及海盐粒子(1.58%),其他未识别的源为30.58%。由此推断陆地污染源对近海海域大气颗粒物的贡献远远高于海盐粒子,因此对渤海周围陆地污染源的控制应得到足够重视。     

上海大气PM2.5来源解析对比:基于在线数据运用3种受体模型

于2014年12月2～24日在上海市城区对大气中细粒子及其化学组分进行了在线连续观测,基于在线数据运用正定矩阵因子分析法(PMF)、化学质量平衡法(CMB)和多元线性模型(ME2)这3种受体模型开展颗粒物源解析并进行相互验证.结果显示,基于在线数据共获得了8类污染源,包括二次硝酸盐、二次硫酸盐、二次有机碳、重油燃烧源、工业源、移动源、扬尘源和燃煤源.其中二次硝酸盐、二次硫酸盐、二次有机碳等二次污染源(44.9%～64.8%)对PM_(2.5)的贡献最大,移动源(16.8%～24.8%)和燃煤源(5.6%～14.9%)的贡献次之,其他源类的贡献相对较小. 3种模型获得的污染源特征组分和来源结果对比表明, 3种模型获得的二次硫酸盐、二次硝酸盐、二次有机碳、移动源的源解析结果较接近,说明模型对这4类源的模拟较好.ME2和PMF模型对燃煤源、扬尘源的拟合结果要好于CMB;工业源则是CMB的结果更好.     

上海市颗粒物的污染源解析

本文使用正交旋转因子分析法对上海市9个采样点的大气颗粒物进行源解析.通过分析得到对上海市大气颗粒物污染的主要贡献因子是燃煤尘、建筑尘(土壤尘)的贡献率为49.46%,冶金尘的贡献率为36.85%,土壤尘的贡献率为6.49%,汽车尘为4.05%,燃油尘为1.19%.将本文的分析结果和他人用投影寻踪回归法(PPR)、化学质量平衡法(CMB)的结果进行比较,结果有较好的一致性.由于本方法对数据的要求不高、假设简单合理、计算过程简单并较易于实现,因此有较好的实际应用价值.     

A factor analysis-multiple regression model for source apportionment of suspended particulate matter

A factor analysis-multiple regression (FA-MR) model has been used for a source apportionment study in the Tokyo metropolitan area. By a varimax rotated factor analysis, five source types could be identified: refuse incineration, soil and automobile, secondary particles, sea salt and steel mill. Quantitative estimations using the FA-MR model corresponded to the calculated contributing concentrations determined by using a weighted least-squares CMB model. However, the source type of refuse incineration identified by the FA-MR model was similar to that of biomass burning, rather than that produced by an incineration plant. The estimated contributions of sea salt and steel mill by the FA-MR model contained those of other sources, which have the same temporal variation of contributing concentrations. This symptom was caused by a multicollinearity problem. Although this result shows the limitation of the multivariate receptor model, it gives useful information concerning source types and their distribution by comparing with the results of the CMB model. In the Tokyo metropolitan area, the contributions from soil (including road dust), automobile, secondary particles and refuse incineration (biomass burning) were larger than industrial contributions: fuel oil combustion and steel mill. However, since vanadium is highly correlated with SO₄²⁻ and other secondary particle related elements, a major portion of secondary particles is considered to be related to fuel oil combustion.     

Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models

Source apportionment study of PM₁₀ (Particulate Matter) in a critically polluted area of Jharia coalfield, India has been carried out using Dispersion model, Principle Component Analysis (PCA) and Chemical Mass Balance (CMB) techniques. Dispersion model Atmospheric Dispersion Model (AERMOD) was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas (LPG) & Diesel Generator (DG) set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM₁₀.     

Some questions about landscape modlling

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大气中挥发性有机化合物(VOCs)的人为来源研究

在2002年春、夏、秋、冬四季对环境大气中挥发性有机化合物的组成和变化规律进行了研究,在此基础上运用CMB8.0受体模型对各类污染源进行了源解析,得到受体点各人为污染源的年平均贡献率分别为:汽车尾气62%,汽油挥发9%,石油液化气10%,涂料6%,石油化工6%,未知源6%.对不同物种贡献的分析显示,环境大气中的乙烯、苯和甲苯等化合物主要来自于汽车尾气的排放,异戊烷来自于汽油的挥发,石油液化气、涂料、石油化工分别对大气中的异丁烷、正己烷和2,4-二甲基戊烷贡献量最大.     

碳化温度对牛粪铜和锌形态及生态毒性的影响

通过设置不同的热解温度(350,550和750℃)对牛粪废弃物进行碳化处理,并使用光谱技术手段对牛粪炭的微观特点及Cu、Zn赋存形态进行了分析表征,同时结合淋溶和毒性实验探究了热解温度对牛粪炭生态毒性的影响.结果表明,高温碳化明显改善牛粪孔隙结构,使其比表面积从牛粪原料的1.15m²/g提高至牛粪炭的5.51(350℃)~195.90m²/g(750℃).随着热解温度的提高,牛粪炭pH值从8.18(350℃)提高到了10.14(750℃);牛粪炭中Cu、Zn含量则从牛粪原料中的1.22和1.23mg/g分别升高至18.29~35.11和18.58~31.24mg/g.透射电镜-选区衍射以及X射线能谱分析表明,热解处理可使牛粪中Cu、Zn离子分别转化为副黑铜矿(Cu₄O₃)和红锌矿(ZnO)等金属氧化物,从而明显降低了牛粪炭中水溶态、DTPA提取态以及HNO₃-H₂SO₄提取态的Cu、Zn离子浓度;此外,FTIR分析及混合有机酸浸提实验结果也表明,350℃牛粪炭中酚羟基、烷烃基、羧基、酰胺类等有机官能团通过吸附和络合作用固定未完全转化的Cu离子,而升高热解温度会使得这些官能团显著减少、促进Cu离子的完全转化以及无机物与Cu、Zn离子之间稳定金属氧化物化合键的形成.淋溶和生态毒性实验表明,高于550℃的热解温度能够显著降低牛粪炭中Cu、Zn的溶出率以及生态毒性,是高Cu、Zn含量牛粪废弃物无害化处理的一种推荐优选技术.     

基于在线监测的南京仙林PM2.5组分特征与来源解析

为了研究南京市PM_2.5的污染特征及来源贡献,于2018年3月至2019年2月在南京仙林地区进行PM_2.5组分的在线监测,运用PMF和CMB受体模型,开展PM_2.5的来源解析.结果表明,观测期间南京市PM_2.5平均质量浓度为54.3μg/m³,其中冬季平均浓度76.4μg/m³.PM_2.5的主要组分为NO₃^-（21.3%~30.8%）、SO₄^2-（18.9%~23.5%）、NH₄⁺（14.3%~16.2%）.从全年平均来看,PMF模型得到的PM_2.5解析结果为：二次无机气溶胶（54.9%）、燃煤源（17.4%）、二次有机气溶胶（7.4%）、机动车排放源（7.1%）、工业源（4.9%）、扬尘源（4.8%）、其他源（3.4%）;CMB模型得到的PM_2.5解析结果为：硝酸盐（33.0%）、硫酸盐（24.0%）、燃煤源（16.4%）、机动车排放源（8.4%）、二次有机气溶胶（7.1%）、扬尘源（5.7%）、其他源（2.9%）、工业源（2.4%）.不同季节PM_2.5来源有所差异,夏冬季二次无机气溶胶占比大于春秋季,春冬季燃煤占比最大,二次有机气溶胶在秋季占比最大.结合2017年南京市大气污染源排放清单,对二次气溶胶贡献进行再解析,得到南京仙林地区PM_2.5主要贡献来自燃煤源（PMF：34.14%,CMB：33.82%）,机动车排放源（PMF：27.33%,CMB：29.33%）以及工业源（PMF：26.76%,CMB：24.77%）.可见,影响南京仙林地区PM_2.5的污染源主要来自燃煤源、机动车排放源和工业源,基于在线组分监测、利用PMF和CMB模型得到的PM_2.5源解析结果具有较好的一致性.     

Anthropogenic contributions to the carbonaceous content of aerosols over the Pacific Ocean

During a joint U.S./U.S.S.R. research cruise from 3 May to 27 July 1987 both total and fine (< 2 μm) particulate material was sampled. This cruise started in the Hawaiian Islands and then proceeded to the Kamchatka peninsula, south to Wellington, New Zealand, south of Australia into the Indian Ocean, to Singapore and then returned to the Hawaiian Islands by sailing just north of the Equator. Particulate samples, collected on quartz fiber and Teflon filters, were analysed for organic and elemental carbon by transmission thermo-optical carbon analysis and for trace elements by energy dispersive X-ray fluorescence.In the Northern Hemisphere the total particulate, organic carbon and elemental carbon concentration ranges were, respectively, 10–25, 0.5–2.5 and 0–0.3 μg m⁻³. In the Southern Hemisphere they were, respectively, 5–10, <0.6 and <0.02 μg m⁻³. In the Northern Hemisphere the fine particulate concentration range was 2–15 μg m⁻³ and the fine fraction varied from 20 to 80% of total aerosol loading. In the Southern Hemisphere the fine particulate loading was 1.2–1.7 μg m⁻³ and was usually less than 20% of the total particulate mass.Chemical mass balance (CMB) modeling was used to determine possible anthropogenic particulate contributions to the ocean aerosol. Readily available source profiles were used for CMB modeling. Sea-salt aerosol was represented by either the conventional EPA marine source profile or by the average of ambient ocean aerosols sampled in very clean mid-ocean regions. Usually 60–90% of ambient particulate mass was “explained” by the CMB model. Sources such as soil, catalytic auto emissions and wood-burning emissions were found to be possible contributors to the ocean aerosol, especially in the Northern Hemisphere. Anthropogenic contributions were estimated to contribute from 10 to 30% of oceanic aerosol mass. Emissions from a hugh forest fire that burned in northern China during the spring of 1987 were possibly detected, but the CMB model cannot distinguish this source from emissions from heating and cooking with wood.Since anthropogenic emissions are mainly combustion emissions which usually contain a large carbon component, carbon data is essential to CMB modeling. The relatively good CMB results obtained in this study suggest that it might be useful to develop source profiles for major emission sources in those countries which contribute most directly to oceanic aerosols. Perhaps characteristic national or regional source profiles could be developed.