CMB模型对污染源成分谱的敏感性分析

进行了源解析化学质量平衡法(CMB)EPA-CMB8.2模型对不同污染源类的敏感性分析。结果表明:CMB模型对道路尘源的敏感性水平为0.90,对木材燃烧源很敏感,对生物质燃烧源的敏感性水平为0.93,且对氯元素的拟合差异较大。将相关性在敏感性水平以上的成分谱通过取其平均值得到新的成分谱,使用新成分谱的解析结果与使用原有成分谱的结果相一致。分析结果为源成分谱的使用提供了依据,拓宽了CMB模型的应用。     

可吸入颗粒上多环芳烃来源的识别和解析

根据化学质量平衡(CMB)受体模型对安阳市大气颗粒物中多环芳烃进行源解析。测定安阳市非采暖季和采暖季可吸入颗粒物中多环芳烃的浓度,对其污染水平进行比较分析。根据污染源调查结果,确定市区多环芳烃的主要排放源类,并建立相应的源成分谱。应用CMB受体模型解析安阳市可吸入颗粒物中多环芳烃主要来源的分担率。     

土壤污染物源解析技术研究进展

简述了土壤污染物源解析技术的发展历程及土壤污染物的主要类型与来源。指出,定性源识别技术主要包括特征比值法、多元统计法、空间分析法等;定量源解析技术主要包括源清单法、扩散模型法、化学质量平衡模型法、正定矩阵因子分解法、UNMIX模型法、同位素法等。重点总结了这些技术方法的原理及其在应用上的优势与局限。从解析对象、解析方法和软件开发角度,提出了土壤污染物源解析技术的未来发展方向。     

辽宁省三城市大气颗粒物来源解析研究

针对辽宁省的沈阳、抚顺、葫芦岛三个城市的大气颗粒物来源,应用CMB化学质量平衡模型和二重源解析技术进行了定性和定量解析。识别各源类及其成分谱的特征,分析、比较大气颗粒物的时空分布特征,并计算三城市各源类在不同季节对城市大气颗粒物污染的贡献率,得到辽宁省城市大气颗粒物来源和季节分布特征的一般规律,说明城市扬尘、土壤风沙尘和煤烟尘是城市颗粒物的主要来源,应作为颗粒物污染治理的重点,而抚顺市特有的钢铁尘,葫芦岛市特有的锌尘等源类的污染也不容忽视。     

沈阳市大气PM2.5中多环芳烃的污染特征及来源解析

采用气相色谱-质谱联用仪定量分析2016年沈阳市PM_(2.5)中16种多环芳烃(PAHs)的质量浓度,探讨其时空分布特征,并解析PAHs的来源。结果表明:沈阳市PAHs的平均质量浓度为71. 5 ng/m3,其中3环、4环PAHs分别占31. 3%和48. 8%;采暖期PAHs浓度明显高于非采暖期,中心城区高于周边。总毒性当量浓度平均值为8. 05 ng/m3。特征比值法和主成分分析法解析的PAHs来源基本一致,主要为燃烧源、石油挥发源和工业生产源,贡献率分别为70. 11%、14. 19%和10. 74%。     

兰州市大气降尘中PAHs分布与生态风险评价

对兰州市春季不同功能区大气降尘有机质中多环芳烃（PAHs）种类进行分析，采用GC／MS法检出了大气降尘中有机质USEPA优控PAHs有11种以上，含量较高的集中于萘（NAP）、荧蒽（FLUA）、菲（PHE）、芘（PYR）、[艹屈]（CHR），占各采样点PAHs总量的70％以上。源解析表明兰州市各功能区大气降尘PAHs来源不仅有较高的燃煤源、交通源（汽车尾气），同时PAHs的组成与分布也在很大程度上受到采样点周围居民居住环境的影响。对照有关的沉积物质量标志水平，兰州市城关区各站点都有PAHs浓度超出相应质量警戒水平，属高生态风险区     

环境空气中多环芳烃(PAHs)不同岗位暴露情况研究

探讨不同岗位暴露多环芳烃（PAHs）情况，结果表明，环境空气中PAHs受岗位影响显著，随暴露程度的加重，PAHs暴露浓度呈明显上升趋势。2、3环的PAHs主要分布在气相中，而4环以上的PAHs则主要存在于PM10中。PAHs相当于BaP当量致癌强度也表明，相对于气相来说，致癌源为PM10，其贡献率为90％。     

不同类型企业周边土壤中多环芳烃来源解析与风险评价

研究对比了山东省不同类型污染企业周边土壤中16种多环芳烃(PAHs)的污染水平,结果表明:化工、钢铁、焦化企业周边土壤中ΣPAHs范围分别为41.4μg/kg～804μg/kg、1 230μg/kg～1 945μg/kg和776μg/kg～1 299μg/kg,土壤中PAHs成分谱轮廓相似,4～6环PAHs占比普遍高于2～3环。特征比值法源解析表明,PAHs主要来源于煤、焦炉、木材等的不完全燃烧。企业周边土壤PAHs污染与企业产业结构有关,钢铁、焦化、石化等大量消耗化石燃料的企业周边土壤中10种PAHs的毒性当量浓度TEQ_(Bap)超标0.6倍～3.8倍,而高分子化工、精细化工、农药化工等企业周边土壤受PAHs污染较轻,均满足荷兰土壤质量标准。     

潍坊市大气环境中PM2.5和PM10的污染特征及来源解析

于2017年1月—2018年1月在潍坊市城区8个监测点位按季节采集了环境空气颗粒物样品，对其组分进行分析；采用电子低压冲击仪（ELPI）稀释采样法和稀释四通道法2种源采样方法同步采集源样品，建立了潍坊市本地化的燃煤源、钢铁源等排放源的颗粒物源成分谱；结合排放源清单，利用化学质量平衡受体模型（CMB）开展不同行业的细颗粒物（PM_2.5）和可吸入颗粒物（PM₁₀）的精细化来源解析。结果表明，各监测点位ρ（PM_2.5）、ρ（PM₁₀）年均值均超过环境空气质量二级标准；潍坊市城市扬尘、土壤风沙尘、建筑水泥尘特征组分分别为硅（Si）、Si、钙（Ca），燃煤尘和造纸碱回收尘的特征组分均为硫酸根离子（SO₄^2-）；PM_2.5首要的贡献源类为煤烟尘，分担率为36%；其次为机动车尘，分担率为25.4%；扬尘的分担率为21.8%；煤烟尘中分担率最高的是工业燃煤（18%）；机动车尘中以载货汽车分担率最大（14%）。PM₁₀首要的贡献源类也是煤烟尘，分担率为30.9%，其次是扬尘（27.6%）、机动车尘（21.5%）；煤烟尘中分担率最高的是工业燃煤，为15.4%，机动车尘中以载货汽车分担率最大，为11.8%。工艺过程的分担率均较低。     

在线分析宁波市北仑区VOCs组成、趋势及来源

利用VOCs在线监测技术，对2010年宁波市北仑区空气内的VOCs的浓度、组成、变化规律及来源进行分析研究。结果表明，在北仑区域内的16种VOCs中，苯、甲苯、二甲苯、乙苯和己烷的比例占到了总数的82．9％，且该5种有机物浓度存在较为典型的季节性变化规律和日变化规律；采用CMD模型法对VOCs的来源进行解析后发现，北仑区域内的VOCs主要来源于汽车尾气、汽油蒸气和石油液化气，而且汽车尾气的贡献值要比一些大城市低得多，且夏季和冬季的成分源贡献率存在明显差异。     

Chemical mass balance model for source apportionment of aerosols in Bombay

Aerosol samples collected within an industrial region of Bombay were analyzed for elemental concentrations using inductively coupled plasma emission spectroscopy, ultraviolet/visible spectrophotometry and X-ray fluorescence spectroscopy. Nineteen elements were selected as tracers of identified sources of aerosol in the region. The U.S. EPA chemical mass balance model was employed for source apportionment. Seven major source types were identified and the performance of the model was evaluated at different sampling locations. Model results were unsatisfactory at highly polluted sites in the study regions. It was found that U.S. EPA source profiles are not suitable for such regions in India and site-specific source profiles should be used in the application of chemical mass balance for source apportionment.     

Application of CMB model for source apportionment of polycyclic aromatic hydrocarbons (PAHs) in coastal surface sediments from Rizhao offshore area, China

Polycyclic aromatic hydrocarbons (PAHs) in coastal surface sediments from Rizhao offshore area were analyzed by gas chromatography–mass spectrometry. A chemical mass balance (CMB) model developed by the U.S. Environmental Protection Agency (EPA), CMB8.2, was used to apportion sources of PAHs. Seven possible sources, including coal residential, coal power plant, diesel engines exhaust, gasoline engines exhaust, coke oven, diesel oil leaks, and wood burning, were chosen as the major contributors for PAHs in coastal surface sediments. To establish the fingerprints of the seven sources, source profiles were collected from literatures. After including degradation factors, the modified model results indicate that diesel oil leaks, diesel engines exhaust, and coal burning were the three major sources of PAHs. The source contributions estimated by the EPA’s CMB8.2 model were 9.25%, 15.05%, and 75.70% for diesel oil leaks, diesel engines exhaust, and coal burning, respectively.     

Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere

This study reports source apportionment of polycyclic aromatic hydrocarbons (PAHs) in particulate depositions on vegetation foliages near highway in the urban environment of Lucknow city (India) using the principal components analysis/absolute principal components scores (PCA/APCS) receptor modeling approach. The multivariate method enables identification of major PAHs sources along with their quantitative contributions with respect to individual PAH. The PCA identified three major sources of PAHs viz. combustion, vehicular emissions, and diesel based activities. The PCA/APCS receptor modeling approach revealed that the combustion sources (natural gas, wood, coal/coke, biomass) contributed 19–97% of various PAHs, vehicular emissions 0–70%, diesel based sources 0–81% and other miscellaneous sources 0–20% of different PAHs. The contributions of major pyrolytic and petrogenic sources to the total PAHs were 56 and 42%, respectively. Further, the combustion related sources contribute major fraction of the carcinogenic PAHs in the study area. High correlation coefficient (R ² > 0.75 for most PAHs) between the measured and predicted concentrations of PAHs suggests for the applicability of the PCA/APCS receptor modeling approach for estimation of source contribution to the PAHs in particulates.     

杭州市大气PM2.5和PM10污染特征及来源解析

2006年在杭州市两个环境受体点位采集不同季节大气中PM_2.5和PM₁₀样品,同时采集了多种颗粒物源类样品,分析了其质量浓度和多种化学成分,包括21种无机元素、5种无机水溶性离子以及有机碳和元素碳等,并据此构建了杭州市PM_2.5和PM₁₀的源与受体化学成分谱;用化学质量平衡(CMB)受体模型解析其来源。结果表明,杭州市PM_2.5和PM₁₀污染较严重,其年均浓度分别为77.5μg/m³和111.0μg/m³;各主要源类对PM_2.5的贡献率依次为机动车尾气尘21.6%、硫酸盐18.8%、煤烟尘16.7%、燃油尘10.2%、硝酸盐9.9%、土壤尘8.2%、建筑水泥尘4.0%、海盐粒子1.5%。各主要源类对PM₁₀贡献率依次为土壤尘17.0%、机动车尾气尘16.9%、硫酸盐14.3%、煤烟尘13.9%、硝酸盐粒8.2%、建筑水泥尘8.0%、燃油尘5.5%、海盐粒子3.4%、冶金尘3.2%。     

Motor Vehicle Contributions to Ambient PM10 and PM2.5 at Selected Urban Areas in the USA

A source apportionment study was carried out to estimate the contribution of motor vehicles to ambient particulate matter (PM) in selected urban areas in the USA. Measurements were performed at seven locations during the period September 7, 2000 through March 9, 2001. Measurements included integrated PM_2.5 and PM₁₀ concentrations and polycyclic aromatic hydrocarbons (PAHs). Ambient PM_2.5 and PM₁₀ were apportioned to their local sources using the chemical mass balance (CMB) receptor model and compared with results obtained using scanning electron microscopy (SEM). Results indicate that PM_2.5 components were mainly from combustion sources, including motor vehicles, and secondary species (nitrates and sulfates). PM₁₀ consisted mainly of geological material, in addition to emissions from combustion sources. The fractional contributions of motor vehicles to ambient PM were estimated to be in the range from 20 to 76% and from 35 to 92% for PM_2.5 and PM₁₀, respectively.     

沈阳市大气中PM2.5来源解析研究

通过2015年在沈阳市采集PM2.5样品及源类样品,分析样品的质量浓度和化学组成,用化学质量平衡(CMB)模型对该市PM2.5来源进行解析。结果表明:沈阳市大气中PM2.5浓度时空变化特征明显;各主要源类对沈阳市PM2.5的分担率依次为煤烟尘(28.03%)、二次无机离子(22.63%)、机动车尾气尘(17.27%)、城市扬尘(13.28%)、建筑尘(5.94%)、土壤风沙尘(5.82%)、道路尘(3.04%)、生物质燃烧尘(2.74%)和冶金尘(1.25%)。燃煤和机动车的有效控制既能降低本类源的贡献,也能降低二次无机离子,体现了多源类综合治理原则。     

同位素技术在环境科学研究中的应用进展

综述了近年来同位素技术在国内外环境科学研究中的应用状况,简要介绍了同位素技术的应用原理与分析方法。总结了放射性同位素技术和稳定同位素技术在环境史重建、污染物示踪及源解析等方面的应用,重点介绍了稳定同位素技术在水、大气和土壤污染物源解析中的应用进展。对未来同位素技术在环境科学研究中的应用进行了展望,提出了多种同位素联合解析、建立和完善定量源解析模型等建议。     

Source apportionment of personal exposure of fine particulates among school communities in India

Source contribution estimates (SCE) of school community personal Respirable Particulate Matter (RPM) have been investigated. Reported relationships of personal RPM with Ambient-outdoors and indoor RPM levels have given the concept of defining the sources of personal exposure. Ambient-outdoors, indoors, soils and local road- traffic dusts were identified as main routes and principal sources of fine particulates at personal exposure levels. Fifteen subjects (05 from each of three schools) were selected from previous conducted study of interrelationships among classified atmospheric receptors in theses schools located in Bhilai-Durg, District Durg, India. Samples of RPM collected from identified receptors and sources were analyzed for selected chemical constituents and the chemical data has been utilized in preparation of source-receptor profiles. Chemical mass balance (CMB8) model has been used for source apportionment study. Major dominating source is ambient-outdoors in case of school located near to steel plant downwind. Indoors and road-traffic dusts have also played dominating role in case of school located near to National Highways. Indoor ventilation properties have played an important role in source contribution estimates.