Primary and secondary components of PM2.5 in Milan (Italy)

In sampling campaigns--carried out by means of a high-volume gravimetric sampler--performed between August 2002 and December 2003, 24-h PM2.5 samples have been collected at an urban background site in downtown Milan and analyzed for elemental and organic carbon, ionic species (i.e., chloride, nitrates, sulfates and ammonium) and some elemental species. Chemical speciation data are evaluated also in terms of primary and secondary components of fine particulate matter: in particular, the contribution of secondary organic aerosols (SOA) and of the primary contribution from traffic to observed PM2.5 concentration levels are evaluated by means of the EC tracer method.     

兰州市低碳数正构烷烃组分特征及大气化学反应活性分析

为明确兰州市大气环境常态下低碳数正构烷烃（C₅~C₁₉）质量浓度变化、组分特征及大气化学反应活性之间的关系,于2017年11月-2018年6月选取兰州市5个采样点,利用TENAX吸附管采集空气样品,应用TD-GC/MS（热脱附-气质联用）法对样品进行分析.对空气样品中低碳数正构烷烃大气化学反应活性的OFP（臭氧生成潜势）、·OH消耗速率、SOA（二次有机气溶胶）生成潜势进行评估计算,通过相关性分析及因子分析法分析低碳数正构烷烃的大气化学反应活性贡献率特征.结果表明:①在10种低碳数正构烷烃中,正己烷（C₆）对OFP的贡献率最大,正辛烷（C₈）对·OH消耗速率的贡献率最大,二者的贡献率分别为37.71%和37.64%.②在10种低碳数正构烷烃中正辛烷（C₈）对SOA生成潜势的贡献率最大,为50.02%.③大气化学反应活性参数相关性分析表明,低碳数正构烷烃总质量浓度与OFP、·OH消耗速率相关性显著（R分别为0.895和0.948）.④因子分析表明,5个未知因子所包含的化学反应活性信息量在94.511%以上,所含信息量也体现了未知因子组成的重要性,可为进一步解析大气化学反应活性提供参考.研究显示,正辛烷、正己烷是低碳数正构烷烃的2个关键活性组分,正辛烷是大气化学反应活性贡献率最大的化合物之一,也是汽车尾气排放源的主要组成部分,正己烷是人为源与自然源的混合产物.      

2017年最大降水对再生水受水河道径流组成的影响

再生水受水河道水文条件作为河流水生环境及生化反应重要控制因子,会受到丰水季降水汇入的影响.以潮白河典型受水河道——顺义段为例,通过丰水期前（2017-06-11）、降水中（2017-07-06）、降水后（2017-07-08、2017-07-09）河水中氢氧同位素特征和氯离子浓度的变化,识别地表径流组成对2017年最大日降水量（重现期3.3a）的响应,揭示河流汇水过程径流组成的时空差异及原因.结果表明,在降水初期,降水中氢氧同位素主要受雨量效应影响,后期微小变幅主要受水汽来源差异影响,整个河段接受降水的同位素值相近.降水后3 d内坡地汇流尚未停止,在各断面占比各异;坡地汇流占比沿程增加（2%~85.6%）,再生水占比沿程减少（90%~67%）,再生水通过优先通道到达下游断面.降水后3 d内SY01~SY05断面水量由坡地汇流、再生水、原位水组成,有明显的河网汇水过程,SY06~SY07断面水量由坡地汇流及原位水组成.     

山西大学城PM2.5中元素特征、来源及健康风险评估

为研究山西大学城PM_2.5中元素的污染特征及来源,采用能量色散X射线荧光光谱仪（energy dispersive X-ray fluorescence spectrometer,ED-XRF）对研究区域2017年PM_2.5样品中21种元素进行分析,并对Mn、Zn、Cu、Sb、Pb、Cr、Co和Ni等重金属进行健康风险评估,同时采用主成分分析方法（principal component analysis,PCA）和正定矩阵因子分解法（positive matrix factorization,PMF）定量解析元素的主要来源.结果表明,2017年山西大学城PM_2.5中21种元素中Ca质量浓度最高,其次是Si、Fe、Al、S、K和Cl,这7种元素占元素总质量浓度的95.71%.其中,Cr元素浓度超过我国环境空气质量标准年平均浓度限值的104倍.春季、夏季和冬季PM_2.5中Ca质量浓度最高,而秋季S元素质量浓度最高.对3类人群具有非致癌风险的元素均为Mn,且风险大小依次为儿童 > 成年男性 > 成年女性;具有可容忍致癌风险的元素为Cr和Co,且风险大小为成年男性 > 成年女性 > 儿童.山西大学城PM_2.5中元素的主要来源包括：天然矿物粉尘和城市扬尘、燃煤和交通源.     

Multivariate analysis of spatial patterns: a unified approach to local and global structures

We propose a new approach to the multivariate analysis of data sets with known sampling site spatial positions. A between-sites neighbouring relationship must be derived from site positions and this relationship is introduced into the multivariate analyses through neighbouring weights (number of neighbours at each site) and through the matrix of the neighbouring graph. Eigenvector analysis methods (e.g. principal component analysis, correspondence analysis) can then be used to detect total, local and global structures. The introduction of the D-centring (centring with respect to the neighbouring weights) allows us to write a total variance decomposition into local and global components, and to propose a unified view of several methods. After a brief review of the matrix approach to this problem, we present the results obtained on both simulated and real data sets, showing how spatial structure can be detected and analysed. Freely available computer programs to perform computations and graphical displays are proposed.     

广州市空气污染物和气象要素的主成分与典型相关分析

用主成分和典型相关分析方法分析广州市近4a(2001—2004年)的空气污染物与气象要素之间的关系,按夏半年、冬半年和全年3个时间尺度分别进行。结果表明:污染物数据所得到的主成分分别代表机动车污染源(汽油燃烧和扬尘)和工业污染源(工业燃煤和燃油),气象数据的主成分分析表明空气的温度、湿度及对流速度对空气污染作用明显,而污染物和气象要素的主成分分析表明气温高低和空气干湿程度对大气污染的影响较大。污染物与气象要素两组数据之间的典型相关分析表明污染物与气象要素之间存在显著的相关关系,其中温度和风速对气态污染物有显著影响。     

大麦秆控藻研究进展

大量实验研究证明：大麦秆是一种很具前景的抑藻物。它能有效控制引起水体富营养化的各种藻类生长，优化水生生物的组成结构。例如水体中放入大麦秆可以增加无脊椎动物以及鱼类的数量，从而达到改善水生生态系统的目的。大麦秆的抑藻机理非常复杂，研究已经排除了营养物质以及大麦秆上遗留杀虫剂对藻的抑制作用。现在除了传统所认为物理和生物机理外，最为认可是化学机理，即大麦秆在有氧降解过程中能产生的诸如酚类、酯类、蒽醌类活性物质，它们能够很好的抑制藻类生长，但是确切的有效化学物质的提取还需要通过GC/MS等方法进一步研究。在小型水体，尤其是鱼类养殖水体中可以直接施用大麦秆以改善水体。总之大麦秆是一种环境友好、生态安全、经济适用的有效控藻物质，应用前景广阔。     

洋河水库水质主成分分析

自20世纪80年代以来,洋河水库每年夏季都爆发“水华”现象,水质不断恶化。为研究水库水体水质年变化情况,于2005年对水库5个采样点的叶绿素a(Chl-a)、总磷(TP)等12项水化学指标进行为期1年的监测。采用基于因子分析的主成分分析方法,将洋河水库水质参数概括为5个主要成分,并分析各个主成分的含义以及其月均值随时间的变化规律。最后结合空间数据,分析了5个主成分年均值的空间分布特征和意义。结果表明,南库区的水质要明显好于北库区。     

用“主成份子集合选择法”优选大气环境测点的缺陷

分析了主成份子集合选择法的应用过程,指出该方法只考虑了各测点的污染随机变化信息.而忽略了各测点的污染水平信息;用该方法所得到的优化点集不能正确地反映地区大气污染水平.因而主成份子集合选择法不宜应用于大气例行监测的优化布点研究中.     

Modeling of NO x Emissions from Oil Refinery Furnaces

The current paper investigates the possibility of establishing an empirically based model for predicting the emission rate of nitrogen oxides (NO _x ) from oil refinery furnaces, in order to continually track emissions with respect to environmental licence limits. Model input data were collected by direct stack monitoring using an electrochemical cell NO _x analyser, as well as a range of telemetry sensors to obtain refinery process parameters. Principal Component Analysis (PCA), in conjunction with Partial Least Squares (PLS) regression was then used to build a series of models able to predict NO _x emissions from the furnaces. The models produced were proven to be robust, with a relatively high accuracy, and are able to predict NO _x levels over the range of operating conditions which were sampled. It was found that due to structural/operational variations a separate model is usually required for each furnace. The models can be integrated with the refinery operating system to predict NO _x emission rates on a continuous basis. Two models representing structurally different furnaces are considered in this paper. This revised version was published online in July 2006 with corrections to the Cover Date.