流域重点工业污染源识别特征成分谱建立技术

研究了基于生产排放全过程、多相、多类污染物并举的污染源识别特征成分谱建立技术。首先建立污染源排放完整图谱,包括原辅料、中间物质、产品、各工艺废水污染物、水处理设施进口和出口污染物等。然后从排放完整图谱中解析出特征污染物。对于废水中的常规污染物和金属污染物,采用与受纳水体浓度相比较的方式得到污染源识别特征污染物,建议将浓度超过受纳水体1倍的污染物定为特征污染物。对于有机污染物,将质量分数之和大于90%的污染物集定为特征有机污染物,并按照有机物类别进行分类。最后开发建立动态的水污染源排放数据库。应用该技术建立了石化行业典型企业的排放特征成分谱,发现这些特征组分具有很好的代表性,为水污染源的识别提供了基础数据。     

大气颗粒物的源成分谱研究现状综述

调研国内外大气颗粒物排放源成分谱研究情况,对排放源的分类、颗粒物的化学组分和测定、各类排放源的成分谱组成、研究情况以及排放源的标识元素情况进行了综述.     

改进二重源解析技术应用中需注意的几个重要问题

对改进二重源解析技术的计算步骤进行了介绍,并对计算中需要注意的几个主要问题进行了阐述,其中包括成分谱的建立、解析参数的范围以及独立源的选取原则。     

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。     

青藏高原典型城市拉萨市大气颗粒物污染源成分谱建立研究与特征分析

系统研究建立高原典型城市拉萨市开放源(土壤风沙尘、道路扬尘、施工扬尘、采矿扬尘),移动源(机动车尾气尘),固定源(工业烟粉尘、生物质燃烧尘及餐饮油烟)共3类8种大气颗粒物(PM_(2.5)、PM_(10))污染源化学成分谱。研究结果表明:开放源以地壳类元素为主,自然背景特征明显;移动源源成分谱中元素碳含量明显高于其他城市,在PM_(2.5)、PM_(10)源谱中分别占60.15%、51.86%,有机碳含量也相对较高,均超过20%;固定源中,牛粪和松柏枝两类生物质燃烧污染源的有机碳含量显著高于其他组分,工业烟粉尘中Ca远高于其他组分,在PM_(2.5)、PM_(10)源谱中分别占21.32%、21.21%。移动源、固定源源成分谱均显示出高原城市的独特特征。     

大气颗粒物中多环芳烃的源解析方法

综述了用于大气颗粒物中多环芳烃（PAHs)源解析的主要定性、定量方法、并对其优缺点作了总结。比值法多用于定性解析，化学质量平衡法（CMB）要求源的成分谱较全面，而多元统计法则要求输入的数据较多。由于缺乏各污染源较完整的PAHs成分谱，且PAHs易发生化学反应，所以CMB法难以广泛推广，而多元统计法对源成分谱，且PAHs易发生化学反应，所以CMB法难以广泛推广，而多元统计不对源成分谱要求低，且不需要考虑PAHs的降解，因而具有推广价值。     

济南市大气颗粒物主要排放源多环芳烃成分谱研究

根据济南市颗粒物排放源的特点,通过采样分析获取了济南市大气颗粒物排放源(土壤风沙尘、扬尘、煤烟尘、机动车尾气尘)的粒度谱、多环芳烃成分谱,为济南市大气颗粒物中多环芳烃的源解析,提供可靠的基础数据。     

北京市主要PM10排放源成分谱分析

对北京市土壤尘、道路扬尘、城市扬尘、建筑施工尘、钢铁尘、煤烟尘等主要PM10无组织排放源和固定源进行采样、分析,建立相应的成分谱数据库,通过对其化学组分分析,确定各类PM10排放源的化学组分特征和标识元素。土壤尘、建筑施工扬尘、钢铁尘、煤烟尘PM10的标识元素分别为Si、Ca、Fe、Al,道路扬尘显示出明显的土壤尘、建筑施工尘和机动车污染的特征,城市扬尘成分谱与道路尘有很强的共线性,具有明显的道路扬尘特征。     

电感耦合等离子体发射光谱法测定土壤和沉积物中硫的适用性研究

为探讨不同消解方式下电感耦合等离子体发射光谱法测定土壤及沉积物中硫的适用性,研究了不同标准物质消解液建立的工作曲线、谱线干扰对分析结果的影响。结合土壤和沉积物来源特点、主成分和待测物含量、干扰物情况,筛选20个典型标准物质分别采用王水水浴法和四酸电热板法进行消解,研究了电感耦合等离子体发射光谱法中3条分析谱线(180.669 、181.972 、182.562 nm)测定样品中硫的情况。结果表明,0.500 0 g样品经10.0 mL 王水(体积比1∶1)沸水浴消解4 h,采用电感耦合等离子体发射光谱仪分析谱线硫181.972 nm建立的18个标准物质工作曲线相关系数高达0.999 9,标准溶液实测浓度相对误差为-6.9%~16%,2个标准物质测定结果的精密度和认定值回收率分别为3.3%~4.3%和90%~96.7%,满足生态环境和自然资源行业的质量控制要求,该方法测定结果与波长色散X射线荧光标准分析方法具有可比性。建立工作曲线的标准物质应考虑待测物和干扰物含量,宜选用标准浓度认定值测试的相对误差而非工作曲线的相关系数作为评价工作曲线优劣的主要技术指标,谱线干扰的理论推导情况和实验结果在趋势上有较好的吻合性。     

机动车排放细颗粒物精细化源谱构建及应用

利用真空瓶现场采样并结合单颗粒气溶胶质谱（SPAMS）技术,对主流品牌汽油车和柴油车排放细颗粒物进行组分分析和源谱构建。结果表明：汽、柴油车尾气源谱特征整体较为相似,相似度均值达到0.88,均以碳和钙为主要特征组分;部分柴油车特征源谱中硫酸盐特征较为明显,碳组分中OC特征突出;汽油车源谱特征中磷酸盐特征相对突出,且碳组分中短链元素碳占比较高。根据建立的源谱进行实际道路观测,结合风速、风向等气象要素分析,2021年2月1日—24日观测点位对应的移动源占比为33.5%,其中汽油车贡献21.0%,柴油车贡献12.5%。     

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.     

A holistic approach combining factor analysis, positive matrix factorization, and chemical mass balance applied to receptor modeling

Rapid urbanization and population growth resulted in severe deterioration of air quality in most of the major cities in India. Therefore, it is essential to ascertain the contribution of various sources of air pollution to enable us to determine effective control policies. The present work focuses on the holistic approach of combining factor analysis (FA), positive matrix factorization (PMF), and chemical mass balance (CMB) for receptor modeling in order to identify the sources and their contributions in air quality studies. Insight from the emission inventory was used to remove subjectivity in source identification. Each approach has its own limitations. Factor analysis can identify qualitatively a minimal set of important factors which can account for the variations in the measured data. This step uses information from emission inventory to qualitatively match source profiles with factor loadings. This signifies the identification of dominant sources through factors. PMF gives source profiles and source contributions from the entire receptor data matrix. The data from FA is applied for rank reduction in PMF. Whenever multiple solutions exist, emission inventory identifies source profiles uniquely, so that they have a physical relevance. CMB identifies the source contributions obtained from FA and PMF. The novel approach proposed here overcomes the limitations of the individual methods in a synergistic way. The adopted methodology is found valid for a synthetic data and also the data of field study.     

北京市大气PM10源解析研究

于2004年在北京市定陵、车公庄、古城、亦庄、房山和奥体中心6个采样点采集大气PM10环境样品,针对北京市颗粒物主要排放源采集土壤尘、建筑水泥尘、燃煤等污染源PM10样品,分别对其中的无机元素、离子、有机碳(OC)和元素碳(EC)进行测定。采用代表北京市颗粒物主要排放源PM10组分特征的成分谱,利用CMB受体模型对PM10来源进行解析。结果表明,PM10的最大来源为土壤尘,其它贡献源类依次为燃煤排放、机动车/燃油排放、二次粒子(SO42-、NO3-和NH4 )、建筑水泥尘。污染源贡献具有明显的季节变化,并存在一定的地域变化。     

Multi-scale Atmospheric Dispersion Modelling by Use of Adaptive Gridding Techniques

An accurate prediction of the transport-reaction behaviour of atmospheric chemical species is required to fully understand the impact on the environment of pollution emissions. Elevated levels of secondary pollutants such as ozone in the lower atmosphere can be harmful to the health of both plants and animals, and can cause damage to property present in the urban environment. Detailed models of pollution mechanisms must therefore be developed through comparisons with field measurements to aid the selection of effective abatement policies. Such models must satisfy accuracy requirements both in terms of the number of species represented, and the spatial resolution of species profiles. Computational expense often compels current models to sacrifice detail in one of these areas. This paper attempts to address the latter point by presenting an atmospheric transport-reaction modelling strategy based upon a finite volume discretisation of the atmospheric dispersion equation. The source terms within this equation are provided by an appropriate reduced chemical scheme modelling the major species in the boundary layer. Reaction and transport discretisations are solved efficiently via a splitting technique applied at the level of the non-linear equations. The solution grid is generated using time dependant adaptive techniques, which provide a finer grid around regions of high spatial error in order to adequately resolve species concentration profiles. The techniques discussed are applied in two dimensions employing emissions from both point and area sources. Preliminary results show that the application of adaptive gridding techniques to atmospheric dynamics modelling can provide more accurately resolved species concentration profiles, accompanied by a reduced CPU time invested in solution. Such a model will provide the basis for high resolution studies of the multiple scale interactions between spatially inhomogeneous source patterns in urban and regional environments.     

BP网络应用于大气颗粒物的源解析

应用BP网络对大气颗粒物进行源解析,将大气采集样本中的元素含量和大气颗粒物源成分谱构成训练样本集,用BP网络进行训练,由训练好的网络的权值可以计算出大气颗粒物的污染排放源的权重贡献率.将BP源解析法的计算结果与其它源解析法得到的结果比较,表明BP网络应用于大气颗粒物的源解析是可行的.     

Measurements of semivolatile and particulate polycyclic aromatic hydrocarbons in a bus station and an urban tunnel in Salvador,Brazil

Motor vehicles constitute a significant source of polycyclic aromatic hydrocarbon (PAH) emissions to the atmosphere. Particle-phase priority pollutant PAH concentrations and total suspended particle mass (TSP) were measured in the Lapa bus station and the Americo Simas Tunnel, located in the city of Salvador, Brazil. Separate samples were collected at the bus station at different times of the day, including rush- and non-rush-hour periods. The highest concentrations for nearly all 16 priority PAHs measured at the bus station were observed at 18:30 h, with chrysene showing the highest mean value (26.6 ng m-3). The highest average PAH concentrations measured in the tunnel were observed for pyrene (79.4 +/- 11.5 ng m-3) followed by fluoranthene (39.0 +/- 5.2 ng m-3) and chrysene (28.0 +/- 4.17 ng m-3). TSP levels reached 423 micrograms m-3 in the bus station, and values as high as 2 mg m-3 in the tunnel. The measured Salvador tunnel PAH profiles are very similar to the Salvador bus station profiles, and are similar to PAH profiles reported for the Kojouike Tunnel, located in Kurashiki City, Japan, and the Caldecott Tunnel, located in Berkeley, California.     

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.     

Critical evaluation of PAH source apportionment tools using data from the Swiss soil monitoring network

In this study a large dataset on the polycyclic aromatic hydrocarbon (PAH) content of Swiss soils was analysed to evaluate two source apportionment tools, i.e., characteristic PAH ratios/molecular markers and a linear mixing model. Population density and total organic carbon (TOC) content were identified by a multiple regression model as independently and positively influencing the PAH concentrations in Swiss background soil. Specifically, TOC was more strongly positively correlated with the sum of light PAH (naphthalene to phenanthrene) than with the sum of heavy PAH (anthracene to benzo[ghj]perylene), whereas population density was more strongly positively correlated with the sum of heavy PAH than with light PAH. In addition, the sum of the heavy PAH as well as the total sum correlated negatively with sample site altitude. It is therefore hypothesised that heavy PAH are less mobile, whereas light PAH were closer to equilibrium with TOC in the soil. Similar results were found for polychlorinated biphenyls (PCB). The characteristic ratios and molecular markers pointed to pyrogenic origin of PAH in Swiss background soil but did not allow for further differentiation of individual fuel contributions, even though attempts to take environmental fractionation processes into account were made. The comparison of three soil profiles identified with a linear mixing model from the pattern of 16 PAH with >300 PAH emission profiles from the literature suggested urban dust, wood combustion and binders from asphalt as PAH sources. However, also here, environmental fractionation processes probably obscured source characteristic PAH ratios and fingerprints, which thus need to be interpreted with caution.