Accumulation and volatilization of different chemical species of selenium by plants
超富集植物吸收富集重金属的生理和分子生物学机制
超富集植物吸收富集重金属的生理和分子生物学机制3李文学 陈同斌33(中国科学院地理科学与资源研究所环境修复室,北京100101)【摘要】 与普通植物相比,超富集植物在地上部富集大量重金属离子的情况下可以正常生长,其富集重金属的机理已经成为当前植物逆境生理研究的热点领域.尤其是近两年,随着分子生物学等现代技术手段的引入,关于重金属离子富集机理的研究取得了一定进展.通过与酵母突变株功能互补克隆到了多条编码微量元素转运蛋白的全长cDNA ;也从分子水平上研究了谷胱甘肽、植物螯合素、金属硫蛋白、有机酸或氨基酸等含巯基物质与重金属富集之间的可能关系.本文从植物生理和分子生物学角度简要评述超富集植物对重金属元素的吸收、富集、螯合及区室化的机制.关键词 超富集植物 重金属 生理学机制 分子生物学机制文章编号 1001-9332(2003)04-0627-05 中图分类号 X171.5 文献标识码 APhysiological and molecular biological mechanisms of heavy metal absorption and accumulation in hyperaccu 2mu altors.L I Wenxue ,CHEN Tongbin (L aboratory of Environmental Remediation ,Institute of Geographical Sciences and N atural Resources Research ,Chinese Academy of Sciences ,Beijing 100101,China ).2Chin.J.A p 2pl.Ecol .,2003,14(4):627~631.In comparison with normal plants ,hyperaccumulators have the ability to accumulate heavy metals in their shoots far exceeding those observed in soil ,without suffering from detrimental effects.With the help of molecular tech 2nologies ,the research on the mechanisms of heavy metal accumulation in hyperaccumulators has been made a great progress.A number of trace element trans porters have been cloned by functional complementation with yeast mutants defective in metal absorption.The relations between glutathione ,phytochelatins metallothioneins ,organic acids and heavy metals have been studied by molecular technologies.This review concentrated on the physiological and molecular mechanisms of heavy metal absorption and sequestration in hyperaccumulators.K ey w ords Hyperaccumulator ,Heavy metal ,Physiological mechanisms ,Molecular biological mechanisms.3国家自然科学基金项目(40071075)、中国科学院知识创新工程重点方向项目(K Z CX 22401202)和王宽诚博士后工作奖励基金资助.33通讯联系人.E 2mail :chentb @ 2002-07-05收稿,2002-11-28接受.1 引 言土壤重金属污染是一个重要的环境问题,传统的治理主要采用物理或化学方法,费用高,对大面积的污染效果差;与传统措施相比,植物修复技术以成本低、操作简单等优点而倍受青睐.广义上的植物修复是指利用植物去除土壤、水体或空气中重金属、有机污染物等污染物的技术,包含植物萃取(Phytoextraction )、根际过滤(Rhizofiltration )、植物挥发(Phytovolatilization )、植物固定(Phytostabilization )等技术,现在通常提到的植物修复主要是指植物萃取[32].超富集植物(Hyperaccumulator )是植物修复的基础,国际上已发现400多种超富集植物,国内对于超富集植物的研究相对较晚,研究较为系统的当属As 、Zn 等重金属的超富集植物[2,3,33].与普通植物相比,重金属离子进入超富集植物体内同样经过吸收/转运、富集/转化/矿化等生理生化过程,而且许多重金属离子进入植物体内的离子通道与必需营养元素相同,这就决定了超富集植物必然具有独特的生理代谢过程.关于这些过程的研究已经成为新的研究热点.本文对有关超富集植物吸收和富集重金属离子的生理及分子机制研究进行评述.2 重金属离子吸收的分子生物学机制 遏蓝菜属(Thlaspi L.)植物具有非常强的富集Zn 的能力,能够在地上部富集高达3%(干重)的Zn ,同时植物正常生长,没有表现出任何中毒症状,它已经成为研究重金属富集机理的模式植物之一.但无论是超富集植物或是普通植物,金属离子进入植物体内的第一步是根系吸收,也就是说吸收过程很可能是超富集植物富集重金属离子的第一个限速步骤.T.caerulescens 与T.arvense 同属于遏蓝菜属,T.caerulescens 能够富集Zn 而T.arvense 则不具此能力,通过比较它们对Zn 2+的吸收动力学发现:两者Km 值差异不大,但T.caerulescens 的Vmax 要比T.arvense 高3.5倍[21],表明T.caerulescens 富集Zn 2+的能力并非是与Zn 2+有更高的亲和力,而很可能是因为锌离子的流入量加大所致,也就是说在T.caerulescens 根系细胞膜上分布有更多的锌离子转应用生态学报 2003年4月 第14卷 第4期 CHIN ESE JOURNAL OF APPL IED ECOLO GY ,Apr.2003,14(4)∶627~631运蛋白.近年来随着分子生物学等现代技术手段的引入,人们对金属离子如何进入细胞有了新的认识.通过对酵母突变株进行功能互补克隆到了多条编码微量元素转运蛋白的全长cDNA,其中研究最多的是ZIP基因家族(ZRT,IRT-like Protein).ZIP基因家族分布非常广泛,在真菌、动物、植物等真核细胞中均发现了ZIP基因家族成员.ZIP基因编码的蛋白一般具有8个跨膜区,C2端和N2端的氨基酸均位于细胞膜外.此家族包含至少25个成员,z rt1、z rt2(zinc2regulated transporter)和irt1(iron2regulated transporter)是最早克隆到的ZIP基因.z rt1、z rt2均由酵母中获得,与Zn的吸收密切相关[36,37];irt1编码的蛋白主要位于拟南芥的根系,体内缺Fe时可诱导irt1表达[8].另一类与金属离子吸收有关的蛋白是Nramp基因家族(Natural resistance associated macrophage proteins).Nramp基因家族编码的蛋白一般具有12个跨膜区,这与ZIP基因家族明显不同.Nramp最初在哺乳动物中发现,植物中的研究主要集中于水稻(Oryz a sativa)和拟南芥(A rabidopsis).O2 ryz a sativa和A rabidopsis的Nramp基因家族分为2类,Os2 Nramp1、OsNramp3和AtNramp5属于一类,OsNramp2、At2 Nramp1、AtNramp2、AtNramp3与AtNramp4属于另一类. Nramp基因家族在植物中的功能现在仍不清楚,AtNramp3和AtNramp4能够维持A rabidopsis体内铁离子的平衡[29].此外,AtNramp3很可能与Ca2+的吸收有关,破坏AtNramp3基因可增加植物对Cd的耐性,过量表达则导致植物对Ca2+的超敏感性.对于超富集植物而言,Zn的吸收过程研究相对较清楚.通过与酵母突变株进行功能互补,Pence等[24]在具有富Zn 能力的T.caerulescen中克隆到z nt1.z nt1编码Zn2+转运蛋白,属ZIP基因家族,缺Zn和Zn供应充足条件下均可以在根系和叶片中高量表达,表明其可能是组成型表达;对于不具有富Zn能力的T.arvense而言,z nt1主要在缺Zn件下表达,供Zn时,表达明显受到抑制.这种表达方式的不同很可能是造成Thlaspi富Zn能力差异的主要原因之一.Assun2 cao等[1]的研究结果也表明Zn转运蛋白基因T.caerulescen 的表达量要远高于T.arvense.从Pence等[24]、Assuncao等[1]与Lasat等[21]的实验结果可以发现根系Zn转运蛋白基因的表达量与Thlaspi富集Zn的能力正相关,初步验证了吸收过程是超富集植物富集重金属离子的首个限速步骤的假设.但是目前还不能肯定转运蛋白是否在超富集植物吸收重金属方面起到决定性作用.譬如说,尽管z nt1、z nt2在T. caerulescen的表达量要远高于T.arvense,但是它们在具有不同富集能力T.caerulescen中的表达量几乎相同[1],即T.caerulescen富集能力的差异与吸收并无太大的相关性.造成此现象的原因很可能在于:(1)一般来说,转运蛋白由一个基因家族控制,而现在得到的克隆还不足以代表整个家族,许多未知的基因可能起到更为重要的作用,如在T. caerulescen就又克隆到z at基因,它与Zn2+的区室化(Se2questration)密切相关,但是此基因与ZIP基因家族明显不同,仅含有6个跨膜区[34];(2)对已知转运蛋白的性质研究还不清楚,金属离子转运蛋白对底物专一性不强,造成多种吸收途径同时对一种金属离子发挥作用,所以在进行具体的分子生物学研究时,必须清楚那些转运蛋白对该金属离子起作用;(3)现在转运蛋白的研究主要集中于根系,叶片中转运蛋白的研究相对较少,但是对超富集植物而言,重金属离子在地上部的含量要远远高于根系,即叶片中的转运蛋白很可能起到更为主要的作用.3 木质部运输 在木质部存在大量的有机酸和氨基酸,它们能够与金属离子结合,这种复合物是重金属离子在木质部中运输的主要形式.譬如在木质部,Fe主要是以柠檬酸铁的形式存在,Zn 主要是与柠檬酸或苹果酸结合,而Cu随着植物不同可与天冬酰胺酸、谷氨酸、组氨酸或烟碱结合,当然也有许多是以离子形态存在的,如Ca、Mg、Mn.在超富集植物中研究较多的为组氨酸.Kramer等[19]发现,组氨酸与A lyssum montanum 富集Ni的能力密切相关,当植物地上部Ni含量高时,木质部中组氨酸含量也较高,外源组氨酸的加入也能显著促进Ni装载入木质部,从而提高Ni向地上部的运输.然而,最近的研究表明,组氨酸反应很可能并不是Ni超富集植物的普遍机理.Persans等[25]在研究Ni的超累积植物Thlaspi geosingense时并没有发现His反应,同时他们克隆了控制His 合成的关键酶基因thg1、thb1、thd1,其表达量并没有随着Ni用量的增加而升高. 重金属由根系进入木质部至少需要3个过程:进入根细胞,由根细胞运输到中柱,装载到木质部.在内皮层由于凯氏带的存在,使得共质体运输在重金属进入木质部的过程中起到主导作用.在共质体运输中起关键作用的是膜转运蛋白,然而直到现在还没有在木质部中克隆到与重金属离子运输相关的基因,这方面的研究,尤其是在研究超富集植物时应该引起充分的重视.与普通植物相比,超富集植物能够高效、迅速地把重金属离子由根系运输到地上部,而通过凯氏带是重金属离子进入木质部主要屏障之一,探明此过程,将有利于提高植物修复的效果.4 对金属离子的解毒机制411 谷胱甘肽(GSH) 许多金属离子是植物必需的微量养分,它们参与植物体内众多的生理代谢过程.但如果含量过高,尤其是具有氧化还原活性的金属,会对植物产生毒害作用,这种毒害作用很可能是由于自由基的形成造成的.GSH含巯基,具有很强的氧化还原特性,可有效地清除活性氧等自由基,因此GSH在植物抗逆境胁迫中起重要作用.GSH为三肽,结构通式为γ2 G lu2Cys2G ly,合成主要通过两步依赖于A TP的反应完成,γ2 EC合成酶和GSH合成酶是其中的关键酶.γ2EC合成酶由gsh1编码,GSH合成酶由gsh2编码,gsh1与gsh2在拟南芥826应 用 生 态 学 报 14卷基因组中均以单拷贝的形式存在. 正常条件下,GSH的合成依赖于半胱氨酸的活性,同时存在明显的反馈抑制现象,表明由γ2EC合成酶催化的反应是整个合成的限速步骤.重金属胁迫条件下,重金属离子激活植物螯合素的合成,消除了GSH的反馈抑制作用,由GSH 合成酶催化的反应也成为限速步骤,此时如果加强gsh2的表达,则既可增加植物螯合素的合成又能避免GSH的耗竭,从而缓解重金属胁迫.Zhu等[38,39]的实验结果验证了此假设.他把大肠杆菌的gsh1与gsh2分别转入到印度芥菜(B rassica juncea),发现印度芥菜对Cd2+的耐性与富集能力均有明显增加,且耐性和富集能力还与gsh2的表达正相关.然而,Foyer等[10]把gsh2转入白杨树(Populus)后,白杨树抗氧化胁迫的能力(光抑制)并没有增加;G oldsbrough等[13]的结果也表明gsh2转入野生型的拟南芥后并不能增加其对Cd的抗性.由此可见,如何通过基因工程改造GSH,以增加植物对重金属的耐性和富集能力还有待于进一步研究.412 植物螯合素(PCs) 植物螯合素(PCs,=cadystins in S.pombe)由植物体内一系列低分子量、能够结合金属离子的多肽组成,其结构通式为(γ2G lu2Cys)n2G ly(图1),一般来讲,n为2~5,最高可达11[5].现已发现多种PC的同功异构体,主要是C端的G ly 被β2Ala、Ser取代形成.原来认为植物螯合素仅存在于植物中,但是随着研究的深入,陆续在线虫、蚯蚓等克隆到PC合成酶的类似基因. PCs不能由基因直接编码,必须在PCs合成酶的催化下完成[14].PC合成酶为四聚体,分子量95000道尔顿,等电点在p H4.8附近,最适反应温度和p H分别为35o℃、7.9[14].然而,由克隆到的编码PCs的全长cDNA推测的结果与此不符,推测结果表明PCs不是多聚体,分子量为42000~70000道尔顿,这种偏差很可能由于在Grill等提纯的酶中PCs并不是主要成分造成的.不同重金属离子诱导PCs合成的能力有很大差别[15],一般为Cd2+>Pb2+>Zn2+>Sb3+>Ag+> Hg2+>As5+>Cu+>Sn2+>Au3+>Bi3+;不同重金属离子诱导PC合成酶活性的能力与诱导PCs合成的能力稍有不同[35]:Cd2+>Ag+>Pb2+>Cu+>Hg2+>Zn2+>Sn2+> Au3+>As5->In3+>Tl3+>G e4+>Bi3+>G a3+.关于PCs 功能研究得相对清楚的是PCs与Cd之间的关系(图2).现图1 植物螯合素的化学结构示意图Fig.1Chemical structure of phytochelatin.已明确PCs在植物解Cd毒中起到重要作用,PCs2Cd复合物是Cd由细胞质进入液泡的主要形式.正是由于PCs在重金属离子区室化中所起的重要作用,近年来PCs已成为植物抗重金属胁迫的研究热点之一. 目前PCs的分子生物学研究基本集中于普通植物或耐性植物,而有关超富集植物的研究相对较少.Schmoger等[28]在用As处理过的蛇根木(Rauvolf ia serpentina)悬浮细胞及拟南芥幼苗中发现了PCs,Hartley2Whitaker等[17]在绒毛草(Holcus lanatus)上也证实了上述现象.但这些植物多属于耐性植物.Ebbs等[7]的实验表明,无论是否具有富集能力, Thlaspi用Cd处理后都会有大量PCs的合成,但是T.ar2 vense中PCs的总量要高于T.caerulescens,说明PCs与植物富Cd能力之间并无太大的关系.由于PCs在超富集植物中的研究还很少,所以PCs在超富集植物是否起到重要作用还有待于深入研究. Cobbett、Rea和等3个研究小组于1999年分别在拟南芥、小麦、酵母中克隆到了编码PC合成酶的全长cDNA.其中,通过对拟南芥cad1突变株(含有与野生型相似的GSH含量,但不含PC)定位克隆获得At PCS1[16],小麦耐Cd基因At PCS1与TaPCS1主要是通过与酵母突变株功能互补得到[4,30].对PC合成酶相应的全长cDNA对齐比较发现其保守区位于N端,同一性高达40%.长时间Cd2+处理cad1突变株也没有发现PCs的合成,表明PCs的合成可能是由单基因控制[18].但随着拟南芥基因组测序的完成,发现了与At PCS1高度同源的At PCS2基因[16],其功能尚不清楚,但与At PCS1相比,其表达量非常低.但植物在长期的进化历程中把At PCS2作为功能基因保留下来,尽管其在正常条件下表达量很低,可以想象在某些器官或环境下,At PCS2基因的表达肯定会起到重要作用.图2 以Cd为例说明谷胱甘肽、植物螯合素在抗重金属胁迫中的作用(+表示增加基因表达或酶活性,-表示减少基因表达或酶活性, HM T1表示位于液泡膜上的PC2Cd转运蛋白),参见Cobbert[5]并作修改Fig.2Function of GSH and PC in the metal tolerance of plants under metal stress(+and2indicate positive and negative regulation of enzyme activities or gene expression,respectively;HM T1is a vacuolar meme2 brane transporter of PC2Cd complex;revised from the figure of Cob2 bert[5]).413 金属硫蛋白(M T) 金属硫蛋白(Metallothioneins)是自然界中普遍存在的一种低分子量、富含半胱氨酸的蛋白质.它与PCs的本质区别在于M T由基因直接编码,而PCs在PCs合成酶的催化下完成.与PCs一样,金属硫蛋白能够通过巯基与金属离子结合,从而降低重金属离子的毒性,它对于Zn2+和Cu2+的解毒效9264期 李文学等:超富集植物吸收富集重金属的生理和分子生物学机制 果尤为明显[23]. 植物中首先鉴定的M T是Ec蛋白,它由小麦成熟胚芽中分离得到.在植物中已发现大约50种M T,根据半胱氨酸残基的排列方式,可以将其分为Ⅰ型、Ⅱ型、Ⅲ型和V型,大多属于Ⅰ型和Ⅱ型.Ⅰ型中的半胱氨酸残基仅有Cys2Xaa2 Cys一种排列方式;Ⅱ型中的半胱氨酸残基有两种排列方式,分别为Cys2Cys、Cys2Xaa2Xaa2Cys.编码I型M T的cDNA 在根系的表达水平较高,编码Ⅱ型M T的cDNA主要在叶片表达. 金属硫蛋白极易水解,尤其植物中的金属硫蛋白氨基酸链比较长,极易在半胱氨酸区水解,同时金属硫蛋白在有氧的条件下非常不稳定,所以难以获得相应蛋白质的资料,目前仅对小麦Ec蛋白及拟南芥M T1、M T2编码的蛋白进行了纯化,这就限制了对M T类似基因功能的研究.Murphy 等[22]证实Cu2+诱导拟南芥M T2表达,而且表达强度与不同基因型抗Cu胁迫的能力密切相关;Nathalie等[13]的研究结果也证实Cu的耐性植物Silene v ulgaris耐Cu胁迫的特性与M T2b的表达紧密联系.王剑虹等[31]在重金属耐性植物紫羊茅草(Festuca rebra)中克隆到mc M T1的全长cD2 NA,此基因编码70个氨基酸,含有12个Cys残基,在N端和C端分别含有3个Cys2Xaa2Cys结构,将此基因转入到酵母M T基因缺失突变株中发现,mc M T1的表达增加了酵母细胞对Cu、Cd和Pb的抗性.在拟南芥和蚕豆中,M T主要在毛状体中表达[9,12],而Cd等许多有毒重金属离子也在毛状体中累积[27],暗示M T和重金属累积有某种联系.414 细胞壁的固持与区室化作用 植物细胞壁残基对阳离子有高亲和力,可以影响重金属离子向细胞内扩散速率,从而影响金属离子的吸收.比较黄花茅(A nthox anthum odoratum)悬浮细胞和原生质体固Pb 能力发现,Pb浓度对从耐Pb细胞克隆分离的悬浮细胞无太大影响,而原生质体的死亡率上升,相应地,从Pb敏感细胞克隆分离的悬浮细胞和原生质体对Pb极其敏感,表明细胞壁在A nthox anthum odoratum抗Pb胁迫中起到重要作用[26].需要明确的是,细胞壁对金属的固定作用不是一个普遍的抗金属毒害的机制,例如抗Zn毒和Zn敏感型菜豆的细胞壁物质表现出相似的亲和力,同时细胞壁有一定的金属容量,而超富集植物能够在地上部富集大量的重金属离子,暗示细胞壁不可能在超富集植物中起到重要作用.最近的研究表明,区室化作用与超富集植物富集重金属离子的能力密切相关.就Thlaspi而言,具有富集能力的T.geosingense液泡中Ni的含量要比不具有富集能力的T.arvense高1倍[20]; Frey等[11]也证实Zn在T.caerulescens中主要分布于表皮细胞液泡中.但区室化作用是否为超富集植物富集重金属离子的一个普遍机理还需对新发现的超富集植物进一步研究才能确定.5 研究展望 关于超富集植物富集重金属离子的研究虽然取得了一定进展,但至今对其分子和生理机制仍不是很清楚,研究人员的看法也存在明显的分歧.在把超富集植物用于实践的过程中,首先要研究清楚对超富集植物富集的生理基础,譬如重金属离子如何进入根细胞,在木质部如何被运输,在叶片中如何分布;其次要注意不同生理过程的联系,就吸收而言,它其实是根系吸收与体内再分配的有机结合,所以在利用基因工程方法增加重金属离子吸收量时,不仅要考虑到增加根系的吸收位点,提高转运蛋白底物的专一性,同时要注意细胞器,尤其是液泡膜上与重金属离子区室化相关膜蛋白的表达,只有这样,才会达到比较好的效果;最后要强调的是学科交叉与渗透,Dhankher等[6]将细菌中的砷酸盐还原酶ArsC 基因和γ2谷氨酰半胱氨酸合成酶(γ2ECS)在拟南芥的叶子中表达,这样运输到地上部的砷酸盐在砷酸盐还原酶的作用下转化成亚砷酸盐,γ2ECS表达可增加一些连接重金属(如亚砷酸盐)并解除其毒性的化合物,将这些复合物限制在叶子中,从而使植物能够积累并忍耐不断增加的As含量.参考文献1 Assuncao A G L,Martins PDC,Polter SD,et al.2001.Elevated expression of metal transporter genes in three accessions of the met2 al hyperaccumulator Thlaspi caerulescens Plant Cell Envi ron,24: 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encodes the low affinity zinc transporter in S accaromyces cerevisiae.J Biol Chem,271: 23203~2321038 Zhu Y L,Pilon2Smits EAH,Jouanin L.1999.Overexpression of glutathione synthetase in Indian mustard enhances cadmium accu2 mulation and tolerance.Plant Physiol,119:73~7939 Zhu Y L,Pilon2Smits EAH,Tarun AS,et al.1999.Cadmium tol2 erance and accumulation in Indian mustard is enhanced by overex2 pressingγ2glutamylcysteine synthetase.Plant Physiol,121:1169~1177作者简介 李文学,男,1973年生,博士后.主要从事植物营养遗传与重金属污染生态学研究,在国内外发表论文8篇. E2mail:liwx@1364期 李文学等:超富集植物吸收富集重金属的生理和分子生物学机制 。
廖晓勇
姓 名 从事专业 工作单位及职位 廖晓勇 性 别 男 出生年月 1977 年 3 月
环境科学与工程 中国科学院地理科学与资源研究所 副研究员
学习及工作经历: (从大学开始填,内容包括时间、单位、学位、所学专业、从事专业、专业技术 职务情况,时间段要连续,准确到月份) 廖晓勇现为中科院地理资源所污染土地修复课题组组长,―污染场地修复 创新联盟‖第一届理事长,(中科院地理资源所-环境保护部华南环科所)城市土 地修复联合研究中心负责人,工业场地污染与修复北京市重点实验室副主任, 北京科学技术研究院客座研究员。 2007 年度入选北京市―科技新星‖计划。 2009 年度获北京青年科技奖。具体经历如下: 2009.7-至今 中国科学院地理科学与资源研究所 2004.8-2009.6 中国科学院地理科学与资源研究所 2001.9-2004.7 中国科学院地理科学与资源研究所 生态学专业 获博士学位 助理研究员 环境科学与工程 副研究员 环境科学与工程
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主要学术成就、科技成果及创新点: (简要概括,不超过 500 字) 主要从事城市工业场地和污染农田土壤的修复技术研究, 是我国较早从事污 染土地修复技术研究和工程示范的学者之一。 在北京、 湖南和广西等地建立了在 环境修复领域有影响力的污染土地修复技术示范工程。 《Environmental Science 在 & Technology》《Environment International》《Science of the Total Environment》 、 、 、 《科学通报》等国内外环境领域重要学术刊物发表论文70多篇,其中第一作者或 通讯作者的SCI收录论文10篇,英文专著1篇,合作培养博士后2名,培养研究生9 名。申请国家发明专利16项,其中9项已经授权。负责或参加20余项重金属污染 土壤修复领域的重要课题,现主持北京市科技计划重大项目、国家863计划课题 和国家自然科学基金项目等课题。主要科技成果及创新点如下: 1. 开发以超富集植物为核心的植物修复技术,并发展植物—微生物联合修 复技术,揭示微生物强化修复的过程与原理。已在湖南、广西和云南等地建立了 修复技术示范工程,污染土壤中砷的年去除效率高达15%,处于国际同类技术的 先进水平。该技术已入选《2010年度国家先进污染防治示范技术名录》 (重金属 污染防治技术领域) 。 2. 研发工业污染场地的化学氧化修复技术和土壤气相抽提修复技术,研制 具有自主知识产权的污染场地修复装备2套,填补国内在该领域的空白,并采用 该技术和装备在北京焦化厂开展中试应用, 修复效果达到预期目标且修复成本要 远低于国际同类技术所需费用,这一成果受到国内外同行的好评。 3. 结合环境科学、地学、土壤学、流行病调查学等方法,对北京、湖南、 广西和甘肃等典型区域进行大尺度土壤污染调查和风险评价, 发现并揭示了我国 工业活动导致大面积土壤重金属污染特征和规律, 这对土壤污染的风险控制和管 理决策提供科学依据。 4. 作为召集人和负责人之一,建立“污染场地修复科技创新联盟” ,该联盟 聚集行业优势力量联合攻关, 构筑污染土地修复产业链以加快修复技术市场化进 程, 为促进污染土地修复领域科技转化为生产力提供基础性平台, 这种科技新模 式得到地方政府的大力支持,并获得国内外同行的高度评价。 科技成果目录: (1.论文作者、年份、题目、期刊名称、卷期、页;2.著作:著者、年份、书名、 出版社;3.专利:名称、专利号、授权时间、负责人;4.其它可以代表申请人科 技贡献的成果) 1. 主持污染土地修复相关课题
IFA国际肥料协会化肥术语中英文(英语)对照
IFA化肥术语(中英对照)
一、肥料施用及相关词汇
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 1
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 2
二、土壤科学及相关词汇
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 3
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 4
三、肥料制造与分析及相关词汇
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 5
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 7
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 8
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 9
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 10
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 11
四、施用机械和方法及其他词汇
中化化肥有限公司全心全意为中国农民服务全国免费服务电话:800-810-9991 12。
不同水氮供应对富士苹果品质产量和果园氮排放的影响
不同水氮供应对富士苹果品质产量和果园氮排放的影响作者:梁洁张馨予于国康赵先飞赵紫嫣刘宇吕中一张林森来源:《果树资源学报》2024年第03期摘要:【目的】针对黄土高原旱地苹果园灌水施肥利用率低、氮排放污染严重等现状,探讨不同水氮供应对富士苹果品质、产量的影响,以及果园土壤氨挥发和氧化亚氮排放上的差异,为农业合理施加氮肥和用水量提供一定的参考。
【方法】于2022年以短枝富士苹果树为材料,以灌水和施氮量为变量,设置5个不同灌水处理:(W5)100%~-90%θ田、(W4)90%~-80%θ田、(W3)80%~-70%θ田、(W2)70%~-60%θ田、(W1)60%~-50%θ田,施氮量设置5个处理:N1(150 kg/ha)、N2(300 kg/ha)、N3(450 kg/ha)、N4(600kg/ha)、N5(750 kg/ha),分析不同处理苹果生理指标、产量性状及土壤氮素排放的变化。
【结果】不同施肥灌水处理下苹果新梢生长长度随灌水量和施氮量的增加而增加;施肥后发生了明显的气态氮排放,在施肥后1周内出现挥发速率峰值。
氮素添加明显增加了NH3挥发累积量,而灌水量的增加则呈现降低趋势。
NH3挥发最大量为W1N5,其整个生育观测收集阶段挥发总量达到了187.07 kg·hm-2;N2O 排放通量的峰值在施氮后立即出现,会在第3~4 d达到排放速率高峰,氮肥施入量越多,N2O 排放损失量越高,施氮量相同的水平下,灌水越少N2O 排放越高。
W1N5的N2O排放速率达到最高,整个生育观测收集阶段排放总量达到了578.92 g·hm-2 ;不同水氮供应的果实横纵径、单果质量、大果率、可溶性固形物质量分数与酸度、产量之间均呈显著(P<0.05)和极显著(P<0.01)正相关关系。
氮素利用率与横径、大果率、可溶性固形物质量分数、产量之间存在显著正相关关系。
因此,水氮利用率与果实品质和产量之间关系密切。
2019年度山东省科技进步奖公示
中国科学院城
6 污染场地铅暴露健康风险评估软件
作权
蔡超,张园,朱永官 市环境研究所
知识产权号 取得日期 国(区)别
CN105606721B
2017 年 10 月 31 日
CN104941583B CN202658158U
2018 年 07 月 17 日
2013 年 1 月 9 日
1.中国 1.中国 1.中国
(3)积极推进技术的落地和转化,山东省分析测试中心技术入股 4500 万,吸引蓝城检测技术 (上海)有限公司投入资金 1 亿元,设立山东蓝城分析测试有限公司。本项目建立的重金属形态和 生物有效性分析方法是入股技术的重要组成部分,在吸引蓝城资金投入过程中发挥了重要作用。
四、客观评价
1、鉴定、验收意见
1、2010年12月10日,受山东省科技厅委托,山东省科学院组织贾瑞宝研究员等国内同行专家, 对本项目中的部分成果进行了鉴定和验收工作,专家一致认为:本项目“南四湖典型水生生物砷污 染分析及其食用风险评价”研究板块,将砷形态分析技术,砷污染状况分析,污染对食品造成的影 响以及食品摄入风险有机结合起来,分析全面,为政府有关部门对食品安全的控制提供了技术支持, 研究成果达到了国内领先水平。
north China
Survey of arsenic and its speciation in rice
8
products such as breakfast cereals, rice
crackers and Japanese rice condiments
七、主要论文、专著目录
序号
论文专著名称
1
Characterization of arsenic biotransformation by a typical bryophyte Physcomitrella patens
(完整版)统计学常用英语词汇
(完整版)统计学常⽤英语词汇Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度⽴体阵Acceleration in an arbitrary direction, 任意⽅向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, ⾃适应估计量Addition, 相加Addition theorem, 加法定理Additive Noise, 加性噪声Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alpha factoring,α因⼦法Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协⽅差分析Analysis Of Effects, 效应分析Analysis Of Variance, ⽅差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, ⽅差分析Angular transformation, ⾓转换ANOV A (analysis of variance), ⽅差分析ANOV A Models, ⽅差分析模型ANOV A table and eta, 分组计算⽅差分析Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area 区域图Area under the curve, 曲线⾯积AREG , 评估从⼀个时间点到下⼀个时间点回归相关时的误差ARIMA, 季节和⾮季节性单变量模型的极⼤似然估计Arithmetic grid paper, 算术格纸Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, ⾮对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近⽅差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, ⾃相关Autocorrelation of residuals, 残差的⾃相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努⼒分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, ⼆元逻辑斯蒂回归Binomial distribution, ⼆项分布Bisquare, 双平⽅Bivariate Correlate, ⼆变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标⽬Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中⼼Centering and scaling, 中⼼化和定标Central tendency, 集中趋势Central value, 中⼼值CHAID -χ2 Automatic Interaction Detector, 卡⽅⾃动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征⽅程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切⽐雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡⽅检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因⼦Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共⽅差Common variation, 公共变异Communality variance, 共性⽅差Comparability, 可⽐性Comparison of bathes, 批⽐较Comparison value, ⽐较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因⼦分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, ⼀致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束⾮线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染⾼斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因⼦Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协⽅差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最⼩⼆乘准则Critical ratio, 临界⽐Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断⾯分析Cross-section survey, 横断⾯调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, ⽴⽅根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输⼊Data-out, 数据输出Dead time, 停滞期Degree of freedom, ⾃由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, ⽆导数⽅法Design, 设计Determinacy, 确定性Determinant, ⾏列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, ⼆分变量Differential equation, 微分⽅程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成⽐例的Disproportionate sub-class numbers, 不成⽐例次级组含量Distribution free, 分布⽆关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, ⽆导数⽅法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第⼀类错误Error type II, 第⼆类错误Estimand, 被估量Estimated error mean squares, 估计误差均⽅Estimated error sum of squares, 估计误差平⽅和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平⾯Expectation surface, 期望曲⾯Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑⽅法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因⼦Factor analysis, 因⼦分析Factor Analysis, 因⼦分析Factor score, 因⼦得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇⾯Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, ⼀阶导数First principal component, 第⼀主成分First quartile, 第⼀四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧⽐率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, ⾼斯增量Gaussian distribution, ⾼斯分布/正态分布Gauss-Newton increment, ⾼斯-⽜顿增量General census, 全⾯普查GENLOG (Generalized liner models), ⼴义线性模型Geometric mean, ⼏何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通⽤线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, ⾏列式的梯度Graeco-Latin square, 希腊拉丁⽅Grand mean, 总均值Gross errors, 重⼤错误Gross-error sensitivity, ⼤错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标⽬Heavy-tailed distribution, 重尾分布Hessian array, 海森⽴体阵Heterogeneity, 不同质Heterogeneity of variance, ⽅差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, ⾼杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直⽅图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, ⽅差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独⽴性Independent variable, ⾃变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, ⽆限总体Infinitely great, ⽆穷⼤Infinitely small, ⽆穷⼩Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初⽔平Interaction, 交互作⽤Interaction terms, 交互作⽤项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可⽐⾏列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动⼒学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯⽶尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis 检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, ⼤样本Large sample test, ⼤样本检验Latin square, 拉丁⽅Latin square design, 拉丁⽅设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最⼩显著差法Least square method, 最⼩⼆乘法Least-absolute-residuals estimates, 最⼩绝对残差估计Least-absolute-residuals fit, 最⼩绝对残差拟合Least-absolute-residuals line, 最⼩绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, ⽔平Life expectance, 预期期望寿命Life table, 寿命表Life table method, ⽣命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然⽐line graph, 线图Linear correlation, 直线相关Linear equation, 线性⽅程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通⽤模型Lognormal distribution,对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最⼩可达⽅差LSD, 最⼩显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标⽬Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极⼤极⼩L 估计量Maximum likelihood method, 最⼤似然法Mean, 均数Mean squares between groups, 组间均⽅Mean squares within group, 组内均⽅Means (Compare means), 均值-均值⽐较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最⼩充分统计量Minimum distance estimation, 最⼩距离估计Minimum effective dose, 最⼩有效量Minimum lethal dose, 最⼩致死量Minimum variance estimator, 最⼩⽅差估计量MINITAB, 统计软件包Minor heading, 宾词标⽬Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重⽐较Multiple correlation , 复相关Multiple covariance, 多元协⽅差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独⽴Natural boundary, ⾃然边界Natural dead, ⾃然死亡Natural zero, ⾃然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, ⽆统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的⾮定常性Nonlinear regression, ⾮线性相关Nonparametric statistics, ⾮参数统计Nonparametric test, ⾮参数检验Nonparametric tests,⾮参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规⽅程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, ⽆效假设Numerical variable, 数值变量Objective function, ⽬标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素⽅差分析Oneway ANOV A , 单因素⽅差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的⾮线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平⾏试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, ⽪尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分⽐Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, ⽪特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平⾯的假设PLANCARDS, ⽣成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并⽅差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, ⼈群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协⽅差Profile trace, 截⾯迹图Proportion, ⽐/构成⽐Proportion allocation in stratified random sampling, 按⽐例分层随机抽样Proportionate, 成⽐例Proportionate sub-class numbers, 成⽐例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有⽬的抽样QR decomposition, QR分解Quadratic approximation, ⼆次近似Qualitative classification, 属性分类Qualitative method, 定性⽅法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, ⽐率Ratio, ⽐例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷⽒检验Rayleigh's Z, 雷⽒Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平⽅和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平⽅和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍⼊Row, ⾏Row effects, ⾏效应Row factor, ⾏因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, ⽰意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, ⼆阶导数Second principal component, 第⼆主成分SEM (Structural equation modeling), 结构化⽅程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯⽶尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯⽪尔曼等级相关Specific factor, 特殊因⼦Specific factor variance, 特殊因⼦⽅差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平⽅根变换Stabilizing variance, 稳定⽅差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因⼦Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学⽣化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平⽅和Sum of squares about regression, 回归平⽅和Sum of squares between groups, 组间平⽅和Sum of squares of partial regression, 偏回归平⽅和Sure event, 必然事件Survey, 调查Survival, ⽣存分析Survival rate, ⽣存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部⾯积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, ⽬标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平⽅和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分⽐趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, ⼆阶最⼩平⽅Two-stage sampling, ⼆阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素⽅差分析Two-way table, 双向表Type I error, ⼀类错误/α错误Type II error, ⼆类错误/β错误UMVU, ⽅差⼀致最⼩⽆偏估计简称Unbiased estimate, ⽆偏估计Unconstrained nonlinear regression , ⽆约束⾮线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, ⽅差⼀致最⼩⽆偏估计Unit, 单元Unordered categories, ⽆序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), ⽅差元素估计Variability, 变异性Variable, 变量Variance, ⽅差Variation, 变异Varimax orthogonal rotation, ⽅差最⼤正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡⽅检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均⽅差Weighted sum of square, 加权平⽅和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
天然气发热量计算标准差异性分析
天然气贸易计量方式有体积、质量和能量计量三种方式,北美、欧洲等国家实施能量计量计价,我国以体积计量方式为主,目前正在逐步向能量计量计价的方式转变。
在能量计量过程中,不同产地、不同气源的天然气因其组分不同,单位体积蕴含的发热量差异较大[1]。
天然气发热量的测定方法分为直接法和间接法。
直接法是将天然气在热量计中燃烧,直接测量其释放的热量的方法。
直接法能够直观地反映出天然气的实际发热量,但是对测量设备要求较高。
我国发热量直接测量技术测量不确定度为0.17%(k =2),达到ISO 15971规定的1级水平,可以满足现场发热量测量结果核查和争议仲裁要求;但未建立ISO 15971标准定义的0级发热量装置,与欧美发热量直接测定不确定度优于0.10%相比还有一定差距[2]。
中国计量科学研究院保存的水流式热量计测量不确定度小于1.0%,不能满足GB/T 18603《天然气计量系统技术要求》中A级站的发热量测天然气发热量计算标准差异性分析李天琦1,2(1.大庆油田设计院有限公司;2.国家石油天然气大流量计量站)摘要:国际上天然气发热量计算标准主要为ISO 6976、GPA 2172以及ASTM D 3588,不同国家计算天然气的发热量选用的计算标准也有所不同。
针对发热量计算标准,分别从标准的适用范围、参比条件、计算方法、计算结果和基础数据引用等方面开展了分析比较。
最后根据不同地域的天然气气质组分数据,对发热量计算结果的差异性开展了的统计分析。
结果显示,不同标准发热量计算值的相对偏差不超过0.0043%,标准偏差为0.0003%。
通过差异性分析为天然气贸易双方对天然气发热量计算标准的选择提供参考。
关键词:天然气;发热量;参比压力;参比温度;ISO 6976;GPA 2172;ASTM D 3588DOI :10.3969/j.issn.2095-1493.2023.10.015Differential analysis of calorific value calculation standards for natural gas LI Tianqi 1,21Daqing Oilfield Design Institute Co .,Ltd.2Nature Gas Large Flow Measurement Station,CNPCAbstract:The international standards for natural gas calorific value calculation are mainly ISO 6976,GPA 2172and ASTM D 3588.The calculation standards used to calculate the calorific value of natural gas vary in different countries.In view of the calorific value calculation standard,the application scope,reference conditions,calculation methods,calculation results and basic data quotation of the standard are analyzed and compared.Finally,according to the data of natural gas composition in dif-ferent regions,statistical analysis is carried out on the difference of calorific value calculation results .The results show that the relative deviation of the calculated calorific value does not exceed 0.0043%,and the standard deviation is 0.0003%.The difference analysis is provided a reference for the selection of natural gas calorific value calculation standards by both sides of natural gas trade .Keywords :natural gas ;calorific value ;reference pressure ;reference temperature ;ISO 6976;GPA 2172;ASTM D 3588作者简介:李天琦,工程师,2014年毕业于东北石油大学(测控技术仪器专业),从事原油、天然气流量仪表检定与技术研究工作,182****6665,***************************.cn,黑龙江省大庆市让胡路区西苑街42号,163000。
施用复合肥对巴戟天产量、养分吸收和寡糖累积量的影响
华南农业大学学报 Journal of South China Agricultural University 2024, 45(1): 71-79DOI: 10.7671/j.issn.1001-411X.202210001冯为迅, 杨源通, 苏立城, 等. 施用复合肥对巴戟天产量、养分吸收和寡糖累积量的影响[J]. 华南农业大学学报, 2024, 45(1): 71-79.FENG Weixun, YANG Yuantong, SU Licheng, et al. Effects of applying compound fertilizer on yield, nutrient absorption and oligosaccharide accumulation of Morinda officinalis[J]. Journal of South China Agricultural University, 2024, 45(1): 71-79.施用复合肥对巴戟天产量、养分吸收和寡糖累积量的影响冯为迅1,杨源通1,苏立城1,盛 晗1,隆曼迪1,储双双2,曾曙才1(1 华南农业大学 林学与风景园林学院, 广东 广州 510642; 2 中山大学 环境科学与工程学院/广东省环境污染控制与修复技术重点实验室, 广东 广州 510006)摘要: 【目的】研究复合肥施用量对巴戟天产量、养分吸收和主要有效成分寡糖累积量的影响,为巴戟天的施肥管理提供理论依据。
【方法】以巴戟天为研究对象,通过盆栽试验,设置每株施用复合肥0、2、4、6、8和10 g (CK、T1、T2、T3、T4和T5)共6个处理,测定巴戟天在不同施肥处理下的产量、养分含量和寡糖含量,分析各指标间的相关性,并运用主成分分析方法进行综合评价。
【结果】施用复合肥可以显著提高巴戟天产量和N、P、K含量,并有利于提高寡糖的累积量。
其中,单株施肥量为6 g处理的巴戟天产量和寡糖总累积量最高,与CK相比,分别提升114.47%和75.36%。
design synthesis and bioolygical evalution of flavonoid salicylate derivatives
b
‡ These authors contributed equally to this work.
inhibiting the hypoxia-inducible factor 1-alpha (HIF-1a) protein level by increasing its degradation and decreasing its stability.10 Substantial studies have been conducted to identify diverse structurally modied avonoids with biological benets superior to their natural counterparts.11,12 Acetylsalicylic acid (ASA) and salicylic acid (SA) which are well known as anti-inammatory drugs also show anti-tumoral properties, induction of apoptosis13,14 and changing tumor glucose utilization.15 The anti-tumor effect of ASA and SA are because of their effects on cyclooxygenase (COX)16 and 6phosphofructo-1-kinase (PFK).15 It has been demonstrated that ASA and SA modulated PFK quaternary structure and decreased tumor cells' glucose consumption and viability, suggesting that ASA and SA could be used as an anti-tumoral agent.15 The structure of the trimethoxybenzene group, which exists in many tumor vascular disrupting agents (Fig. 1), such as CA4P and its analogues OXi4503, AVE8062,17–20 the colchicine analog ZD6126,21 BNC-105 (ref. 22) and CKD-516,23 has an important research value in tumor drug design. Based on the previous considerations, it was proposed that introducing trimethoxybenzene groups and SA and its diverse derivatives into a avonoid scaffold might be an effective strategy for seeking novel avonoid derivatives with potential anti-tumor activity. Therefore, a series of avonoid salicylate derivatives were synthesized which contained trimethoxybenzene and a series of chrysin salicylate derivatives, and these were evaluated for anti-tumor bioactivities in vitro and in vivo.
计量经济学中英对照词汇
计量经济学中英对照词汇Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additive Noise, 加性噪声Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alpha factoring,α因子法Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis Of Effects, 效应分析Analysis Of Variance, 方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型ANOVA table and eta, 分组计算方差分析Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area 区域图Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interaction Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Collinearity, 共线性Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照, 质量控制图Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation, 相关性Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Crosstabs 列联表分析Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve Estimation, 曲线拟合Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Direct Oblimin, 斜交旋转Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Error Bar, 均值相关区间图Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explained variance (已说明方差)Explanatory variable, 说明变量, 解释变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查Generalized least squares, 综合最小平方法GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 通用线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点High-Low, 低区域图Higher Order Interaction Effects,高阶交互作用HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Image factoring,, 多元回归法Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K-Means Cluster逐步聚类分析K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显着差法Least square method, 最小二乘法Least Squared Criterion,最小二乘方准则Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Leveage Correction,杠杆率校正Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显着差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形MSC(多元散射校正)Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal P-P, 正态概率分布图Normal Q-Q, 正态概率单位分布图Normal ranges, 正常范围Normal value, 正常值Normalization 归一化Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Pareto, 直条构成线图(又称佩尔托图)Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式PCA(主成分分析)Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 构成图,饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡PLS(偏最小二乘法)Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal axis factoring,主轴因子法Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和residual variance (剩余方差)Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system , SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequence, 普通序列图Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significant Level, 显着水平Significance test, 显着性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Test(检验)Test of linearity, 线性检验Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Unweighted least squares, 未加权最小平方法Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
异质波动与股票收益:基于中国股市的检验
异质波动与股票收益:基于中国股市的检验刘方池;宋子玉【摘要】基于中国上市公司数据,本文考察一类新兴的股市异象,即“异质波动”之谜。
我们研究发现,在中国资本市场,“异质波动”之谜依然显著存在。
与经典金融理论预期不同,低异质波动的公司收益显著高于高异质波动公司;不同异质性波动风险组合之间的累积收益之差将随着投资期间增加呈现较大差异;而且基于Fama-Macbeth两阶段横截面检验表明,异质波动因子的溢价水平显著为负。
从投资实践的角度而言,本文研究也为构造超额收益的组合提供了经验参考。
%Based on the data of Chinese listed companies, the paper investigats an emerging phenome-non, namely idiosyncratic volatility.We find that,the mystery of “Idiosyncratic volatility” significantly exits in China’ s capital market.It is contradictory with traditional finical theory that corporations with a lower idio-syncratic volatility perform significantly higher return than corporations with a high idiosyncratic volatility;the accumulative return of different risk portfolio with different idiosyncratic volatility shows an expanded trend a-long with the prolong of investment period.Furthermore, Fama-Macbeth model test shows that idiosyncratic volatility has a significantly negative effort on stock return.From a point of the investment practice, the paper provids a reference experience about constructing an portfolio of abnormal return.【期刊名称】《华中科技大学学报(社会科学版)》【年(卷),期】2015(000)003【总页数】7页(P63-69)【关键词】异质波动;股票收益;中国股市【作者】刘方池;宋子玉【作者单位】华中科技大学经济学院,湖北武汉430074;华中科技大学经济学院,湖北武汉430074【正文语种】中文【中图分类】F830.91一、导言风险资产定价是金融理论的重要研究内容。
化学与社会课程论文
化学与社会课程论文:土壤的重金属化学污染及其防治技术南昌大学孤城-凊笛摘要:随着重金属的使用和废弃,其对土壤、水体等的污染造成很大的危害,并严重威胁着环境和人类的发展。
本文通过土壤环境这一典型例子,分析了重金属污染的现状与危害,并介绍化学修复、植物修复、微生物修复等现在常见的土壤修复方法,对土壤的重金属污染治理前景进行展望。
关键词:土壤污染;重金属;土壤修复技术Soil pollution has become one of the global environmental problems,in which contamination by heavy metals occupied much proportion and has a very serious threat to the human s life and the entironment.This article analyses the reason which causes the heave mental pollution at first,then elaborates the remediation technology and research progress of the contaminated soils by heavy metals from engineering measures,physical chemical remediation,chemical remediation,bioremediation,nature remediation,and their c...土壤污染已经成为一个全球性的环境问题之一,而重金属污染在土壤污染中占有很大的比重,对人类生产和生活以及生态环境造成很大的威胁。
文中先简单的阐述了什么事重金属污染,然后针对造成土壤重金属污染的原因进行了分析,后从工程措施、物化修复、化学修复、生物修复、自然修复、及组合修复技术等几个方面分别阐述了重金属污染土壤的修复技术及其研究进展,给出了今后进行重金属污染修复的建议。
土壤重金属污染修复
土壤重金属污染修复摘要:土壤重金属污染问题日益严重,已成为全球环境关注的焦点。
随着土地资源的匮乏,有效土地的需求,对受重金属污染的土壤进行修复成为可安全利用的土地是我们亟待解决的问题。
本文介绍了重金属污染修复技术的原理、方法优缺点并重点介绍了生物修复技术。
关键词:土壤;重金属污染;生物修复1前言土壤是人类生态环境的重要组成部分,是人类赖以生存、生产、生活的主要自然资源之一。
但随着经济的发展,特别是工矿业的迅猛发展,土壤重金属污染日益严重,已成为一个全球性的环境问题。
土壤重金属污染来源广泛,包括采矿、冶金、化工等工业排放的三废和汽车尾气,以及农药和化肥的施用等。
目前,全世界平均每年向土壤系统排放大量重金属,如Pb500万t、Hg约1万t,Cu340万t、Mn1500万t[4]。
土壤重金属污染具有隐蔽性、长期性和不可逆性的特点,直接或者间接地污染地下水、空气,危害农作物及生物,甚至危及人类的健康和生命。
因此,重金属污染防治以及土壤重金属污染的修复,恢复土壤原有功能,一直是国内外研究的热点课题。
纵观近年来国内外关于土壤重金属污染的修复,一般主要采用物理、化学和生物修复方法,通过以下途径对重金属污染的土壤进行修复。
①稀释法,即降低土壤中重金属污染的浓度;②改变重金属形态,使其固定或钝化从而降低其在环境中的迁移性和生物可利用性;③从土壤中去除重金属。
本文将从近年来土壤重金属污染的修复技术原理、特点做分析与比较,并在此基础上着重阐述生物修复技术。
在土壤污染日益严重,土地资源日益匮乏的今天,被重金属污染的土壤修复无疑是一个很重要而且很迫切的问题。
对重金属污染失去农业及其他利用价值的土壤进行修复,缓解日益紧张的土地利用具有十分重要的意义。
2 土壤重金属污染修复方法2.1 物理修复物理修复是最先发展的修复技术之一,包括工程、热解析和电动修复等。
2.1.1 工程修复法修复土壤重金属污染的工程措施主要包括深耕翻土、换土、客土等方法。
土壌汚染浄化ppt
In-situ insolubilization Heavy metals
<2nd Std.* >2nd Std.
VOCs
<2nd Std. >2nd Std.
Agricultural chemicals, etc
<2nd Std. >2nd Std.
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In-situ shuttingin
Shutting-in with water barrier
Cutting off
Evacuation and In-situ remediation
* 2nd Std. : Secondary elution standard
(Dioxins, PCBs, Agricultural chemicals…)
Soil elution standard and soil content standard under the Soil Pollution Control Law
Controlled substance Dichloromethane Carbon tetrachloride 1,2-Dichloromethane 1,1-Dichloroethylene cis-1,2-Dichloroethylene 1,1,1-Trichloroethane 1,1,2Trichloroethane Trichloroethylene Tetrachloroethylene Benzene 1,3-Dichloropropene Cadmium Lead Hexavalent chromium Arsenic Total mercury Alkyl mercury Selenium Fluorine Boron Cyanogen PCB Thiuram Simazine Thiobencarb Organic phosphorus Soil elution standard (mg/L) 0.02 0.002 0.004 0.02 0.04 1 0.006 0.03 0.01 0.01 0.002 0.01 0.01 0.05 0.01 0.0005 Not to be detected 0.01 0.8 1 Not to be detected Not to be detected 0.006 0.003 0.02 Not to be detected Secondary elution standard (mg/L) 0.2 0.02 0.04 0.2 0.4 3 0.06 0.3 0.1 0.1 0.02 0.3 0.3 1.5 0.3 0.005 Not to be detected 0.3 24 30 1 0.003 0.06 0.03 0.2 1 Soil content standard (mg/kg) - - - - - - - - - - - 150 150 250 150 15 - 150 4,000 4,000 50 (free cyanide) - - - - - Classification
The Forms of Capital
The Forms of CapitalPierre Bourdieu 1986.The social world is accumulated history, and if it is not to be reduced to a discontinuous series of instantaneous mechanical equilibria between agents who are treated as interchangeable particles, one must reintroduce into it the notion of capital and with it, accumulation and all its effects. Capital is accumulated labor (in its materialized form or its …incorporated,‟ embodied form) which, when appropriated on a private, i.e., exclusive, basis by agents or groups of agents, enables them to appropriate social energy in the form of reified or living labor. It is a vis insita, a force inscribed in objective or subjective structures, but it is also a lex insita, the principle underlying the immanent regularities of the social world. It is what makes the games of society – not least, the economic game –something other than simple games of chance offering at every moment the possibility of a miracle. Roulette, which holds out the opportunity of winning a lot of money in a short space of time, and therefore of changing one‟s social status quasi-instantaneously, and in which the winning of the previous spin of the wheel can be staked and lost at every new spin, gives a fairly accurate image of this imaginary universe of perfect competition or perfect equality of opportunity, a world without inertia, without accumulation, without heredity or acquired properties, in which every moment is perfectly independent of the previous one, every soldier has a marshal‟s baton in his k napsack, and every prize can be attained, instantaneously, by everyone, so that at each moment anyone can become anything. Capital, which, in its objectified or embodied forms, takes time to accumulate and which, as a potential capacity to produce profits and to reproduce itself in identical or expanded form, contains a tendency to persist in its being, is a force inscribed in the objectivity of things so that everything is not equally possible or impossible.[1] And the structure of the distribution of the different types and subtypes of capital at a given moment in time represents the immanent structure of the social world, i.e. , the set of constraints, inscribed in the very reality of that world, which govern its functioning in a durable way, determining the chances of success for practices.It is in fact impossible to account for the structure and functioning of the social world unless one reintroduces capital in all its forms and not solely in the one form recognized by economic theory. Economic theory has allowed to be foisted upon it a definition of the economy of practices which is the historical invention of capitalism; and by reducing the universe of exchanges to mercantile exchange, which is objectively and subjectively oriented toward the maximization of profit, i.e., (economically) self-interested, it has implicitly defined the other forms of exchange as noneconomic, and therefore disinterested. In particular, it defines as disinterested those forms of exchange which ensure the transubstantiation whereby the most material types of capital – those which are economic in the restricted sense –can present themselves in the immaterial form of cultural capital or social capital and vice versa. Interest, in the restricted sense it is given in economic theory, cannot be produced without producing its negative counterpart, disinterestedness. The class of practices whose explicit purpose is to maximize monetary profit cannot be defined as such without producing the purposeless finality of cultural or artistic practices and their products; the world of bourgeois man, with his double-entry accounting, cannot be invented without producing the pure, perfect universe of the artist and the intellectual and the gratuitous activities of art-for-art‟s sake and pure theory. In other words, the constitution of a science of mercantile relationships which, inasmuch as it takes for granted the very foundations of the order it claims toanalyze – private property, profit, wage labor, etc. – is not even a science of the field of economic production, has prevented the constitution of a general science of the economy of practices, which would treat mercantile exchange as a particular case of exchange in all its forms.It is remarkable that the practices and assets thus salvaged from the …icy water of egotistical calculation‟ (and from science) are the virtual monopoly of the dominant class – as if economism had been able to reduce everything to economics only because the reduction on which that discipline is based protects from sacrilegious reduction everything which needs to be protected. If economics deals only with practices that have narrowly economic interest as their principle and only with goods that are directly and immediately convertible into money (which makes them quantifiable), then the universe of bourgeois production and exchange becomes an exception and can see itself and present itself as a realm of disinterestedness. As everyone knows, priceless things have their price, and the extreme difficulty of converting certain practices and certain objects into money is only due to the fact that this conversion is refused in the very intention that produces them, which is nothing other than the denial (Verneinung) of the economy. A general science of the economy of practices, capable of reappropriating the totality of the practices which, although objectively economic, are not and cannot be socially recognized as economic, and which can be performed only at the cost of a whole labor of dissimulation or, more precisely, euphemization, must endeavor to grasp capital and profit in all their forms and to establish the laws whereby the different types of capital (or power, which amounts to the same thing) change into one another.[2]Depending on the field in which it functions, and at the cost of the more or less expensive transformations which are the precondition for its efficacy in the field in question, capital can present itself in three fundamental guises: as economic capital, which is immediately and directly convertible into money and may be institutionalized in the forms of property rights; as cultural capital, which is convertible, on certain conditions, into economic capital and may be institutionalized in the forms of educational qualifications; and as social capital, made up of social obligations (…connections‟), which is convertible, in certain conditions, into economic capital and may be institutionalized in the forms of a title of nobility.[3]资本可以以三种基本的外表来呈现自身,这取决于它起作用的场域,而且那多或少昂贵的转换预先决定了资本在被讨论的场域中是否有效,例如,经济资本能直接无碍地转化为金钱并能以财产权的形式被体制化;文化资本在某些特定情况下可以被转化为经济资本并能以教育资格证明(educational qualifications)的形式被体制化;社会资本由社会责任[obligations](即联系connection)组成,在特定情况下能被转换成经济资本,且能以荣誉头衔的形式被体制化。
基于超级站观测的兰州大气挥发性有机污染物特征及对臭氧形成的影响
DOI:10.7524/j.issn.0254-6108.2023062901常毅, 刘文君, 周惜荫. 基于超级站观测的兰州大气挥发性有机污染物特征及对臭氧形成的影响[J]. 环境化学, 2024, 43(3): 1025-1034. CHANG Yi, LIU Wenjun, ZHOU Xiyin. Characteristics of atmospheric volatile organic compounds and their relationship with ozone concentration in Lanzhou based superstation observation[J]. Environmental Chemistry, 2024, 43 (3): 1025-1034.基于超级站观测的兰州大气挥发性有机污染物特征及对臭氧形成的影响常 毅1 刘文君1 周惜荫2,3 *(1. 甘肃省环境监测中心站,兰州,730000;2. 中国科学院西北生态环境资源研究院/中国科学院寒旱区陆面过程与气候变化重点实验室,兰州,730000;3. 中国科学院大学,北京,100049)摘 要 近年来随着兰州市经济社会的不断发展,臭氧(O3)和挥发性有机物(VOCs)污染现象日益严重,成为制约兰州市空气污染治理的重要瓶颈. 为合理有效地治理兰州市O3和VOCs污染,兰州大气组分超级监测站构建全方位立体管控体系,观测与分析2021年兰州市O3及其前体物VOCs的浓度分布特征,并利用正交矩阵因子分析(PMF)模型和臭氧生成潜势(OFP)分别分析了VOCs的来源及对臭氧生成的贡献. 兰州市O3浓度夏季最高,春季和秋季稍低,冬季最低,夏季光化学污染高发是导致O3含量较高的主要原因. VOCs污染集中在秋冬季,可能原因为冬季光化学反应减少、积累效应增加和燃煤排放增多使得VOCs含量高. 大气VOCs组成较为稳定,主要为含氧VOCs(OVOCs,35.7%)和烷烃(30.8%). 利用PMF源解析模型确定了当地VOCs的主要贡献源为机动车排放源(27.1%)、化石燃料燃烧源(23.8%)、化工工艺源(17.9%)、汽油挥发源(16.0%)、溶剂使用源(10.7%). 通过对比VOCs的OFP,发现乙烯、丙烯、甲苯对臭氧生成潜势贡献较大,在臭氧污染治理中应重点关注.关键词 臭氧,挥发性有机物,臭氧生成潜势,源解析.Characteristics of atmospheric volatile organic compounds and theirrelationship with ozone concentration in Lanzhoubased superstation observationCHANG Yi1 LIU Wenjun1 ZHOU Xiyin2,3 *(1. Gansu Environmental Monitoring Center Station, Lanzhou, 730000, China;2. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences/Key Laboratory of Land Surface Process and Climate Change in Cold and AridRegions, Chinese Academy of Sciences, Lanzhou, 730000, China;3. University of Chinese Academy of Sciences, Beijing,100049, China)Abstract In recent years, with the economic and social development of Lanzhou City, ozone (O3) and volatile organic compounds (VOCs) pollution has become increasingly serious and restrict the control of air pollution in Lanzhou. In order to control the pollution of O3 and VOCs in Lanzhou reasonably and effectively, an all-dimensional control system was built in Lanzhou Atmospheric Component Super Monitoring Station to characterize of O3 and its precursors (VOCs) in 2021 in Lanzhou. Positive matrix factorization (PMF) model and ozone formation potential (OFP) were used to analyze the sources of VOCs and the contribution of VOCs to ozone formation respectively. In Lanzhou, O3 level was the highest in summer, slightly lower in spring and autumn, and the lowest in2023 年 6 月 29 日 收稿(Received:June 29,2023).* 通信联系人 Corresponding author,E-mail:*****************1026环 境 化 学43 卷winter. The high incidence of photochemical pollution in summer was the main reason for the high O3 level. VOCs concentration was relative higher in autumn and winter, and the possible causes were the decrease of photochemical reaction, the increase of accumulation effect and coal-burning emission made high VOCs level. The components of VOCs were relatively stable, dominating by OVOCs (35.7%) and alkanes (30.8%). PMF model results showed that motor vehicle emission source(27.1%), fossil fuel combustion source (23.8%), chemical process source (17.9%), gasolinevolatilization source (16.0%) and solvent use source (10.7%) were the main contributors to VOCs in this area. By comparing the OFP of VOCs, ethylene, propylene, and toluene played an important role in the ozone formation potential, which should be paid more attention during ozone pollution control.Keywords O3,volatile organic compounds,ozone formation potential,source apportionment.挥发性有机物(volatile organic compounds,VOCs)为饱和蒸汽压在标准状况下大于133.3 Pa、沸点低、分子量小、常温状态下易挥发的有机化合物[1]. VOCs来源丰富且复杂,来源包括生物源(如植物排放和火山爆发等)和人为源(如溶剂挥发、燃料燃烧和机动车排放等),其中城市地区主要来自于人为源的VOCs因为其危害较大而受到广泛关注. VOCs的危害性主要分为两个方面:一是环境影响,VOCs能发生氧化反应,产生臭氧(O3)和二次有机气溶胶(SOA)从而影响空气质量[2 − 3],具体表现为在对流层中,光化学反应活性较强的VOCs物种既可在光照条件下与NO x(氮氧化物)发生反应生成O3[4 − 5],也可与大气中的氧化剂(如羟基自由基等)发生反应生成较弱挥发性的VOCs,进而经过吸附等物理过程分散到颗粒相中转化为SOA[6];另一方面,部分VOCs会损害人体的生理功能和免疫系统等,例如苯、甲苯、乙苯和二甲苯(BTEX)和卤代烃会损害神经和造血系统,甚至导致癌症[7 − 8]. 因此,更好地了解大气中VOCs的特征,是深入分析VOCs对O3、SOA污染形成和人类健康影响的关键.VOCs和O3是我国城市大气复合污染中的重要组成部分. 与其他大气污染物相比,O3污染问题更加复杂,治理难度更大、周期更长[9]. 西北地区大气环境特殊,沙尘量高且湿度较低,近年来大气二次污染程度不断加剧,防治形势严峻[10]. 胡琳等[11]在西北城市O3污染的研究中指出当温度高于30 ℃、相对湿度低于60%时,容易出现高浓度臭氧现象. 刘松等[12]研究了2013—2016年西安O3时空变化特性与影响因素,发现高浓度O3主要出现在高温度、低湿度且风向为东南风或南风的天气背景下. 除气象因素外,许多研究也关注了人为排放和大气化学方面对臭氧的影响. 张瑞欣[13]研究西北工业城市乌海市臭氧污染成因时指出VOCs排放与O3污染高度相关,而VOCs主要来源包括工艺过程源、生物质燃烧源、炼焦及精细化工源、非金属制品源. 曹泽磊[14]研究西安市高新区O3及其前体物污染时指出,机动车尾气排放、汽车维修与喷漆过程排放是VOCs和臭氧浓度较高的主要原因. 兰州市是甘肃省政治经济中心,主体产业为石化与冶炼. 主城区地形上两山环绕南北,海拔约1520 m,河谷型地形地貌导致扩散条件差,区域内光照较强,极易发生光化学反应造成臭氧污染. 上世纪70、80年代,兰州西固区就发生过由高浓度VOCs引起的光化学烟雾现象[15]. 随着城市化进程的加快,兰州市城区环境形势愈发严峻,2019年兰州市轻度(含)以上污染中,以臭氧为首要污染物的天数占36.6%[16]. 近年来兰州大力整治石化行业VOCs排放,大气臭氧浓度却没有得到有效控制,2022年夏季臭氧平均质量浓度为151 µg·m−3,接近空气质量二级标准,可见兰州市大气VOCs污染已经转变为复合污染源排放型. 鉴于兰州市VOCs与O3污染来源和成因的复杂性,为实现兰州市整体O3污染治理与管控,进行大气VOCs与O3污染精细化特征分析及来源解析十分必要.兰州大气组分超级监测站以臭氧污染机理研究为目的,实现兰州市城关区大气污染立体监测,构建“天、地、空”全方位立体管控体系,为研判臭氧污染演变趋势、预警决策、科学治理及区域联防联控提供技术支持. 本研究通过监测2021年兰州市大气O3、VOCs和其他大气组分,开展O3和VOCs污染特征研究,同时评估其臭氧生成潜势(ozone formation potential,OFP),利用正交矩阵因子模型(positive matrix factorization,PMF)对VOCs进行来源解析,为兰州市大气环境O3和VOCs污染管控提供数据基础和科学支撑.1 研究方法(Research method)1.1 采样地点本研究观测实验点位于甘肃省兰州市城关区兰州大学学生活动中心顶楼(103.87° E ,36.05° N ),是甘肃省第一个大气综合观测超级监测站. 该点位于城市政治、经济、文化中心的混合区域,周边2 km 范围内分布有居民区、学校和商业区,西侧毗邻贯穿城市南北的主干道天水路,距中国西部最大的石油化工基地、城市核心工业区西固区约22 km. 观测点配置挥发性有机物连续自动监测系统、过氧乙酰硝酸酯自动监测仪、氮氧化物自动监测仪、紫外辐射计、总辐射计、臭氧激光雷达等监测仪器.1.2 采样分析方法研究采用杭州谱育科技发展有限公司2000-315L 型在线监测仪器、Metcon 公司PAN 、KIPPZONEN 公司紫外辐射计、赛默飞世尔公司42i 型NO/NO 2/NO x 在线监测仪器开展24 h 连续分析. 监测的挥发性有机物组分共108种,包括13种含氧有机物、17种芳香烃、35种卤代烃、31种烷烃、11种烯烃和1种硫化物等. 监测期间严格按照中国环境监测总站《国家环境空气监测网环境空气挥发性有机物联系自动监测质量控制技术规定(试行)》(总站气函2019〔785〕号)文件开展质控工作.1.3 正交矩阵因子分解法(PMF 受体模型)PMF 是一种根据长时间序列的受体点物种组分数据对物种来源进行识别和定量的多元统计模型[17],该模型假设污染物环境浓度是不同污染源排放的各污染物组分的线性加和,基于监测点的观测数据,运用最小二乘法估算污染源的组成及其贡献率[18]. 本研究使用美国环境保护署推荐的PMF 5.0模型对VOCs 进行来源解析. PMF 模型将输入的数据分成因子贡献矩阵和因子成分谱矩阵. 因此,PMF 模型可以表示为:X =GF +E (1)式中,X 为数据矩阵;G 为因子贡献矩阵;F 为因子成分谱矩阵;E 为残差矩阵. G 和F 是根据不确定度迭代计算使目标函数Q 达到最小而得到的,Q 值计算公式为:Q =∑n i =1∑m j =1[x i j −∑p k =1g ik f k j u i j]2(2)式中,n 为样本总数,m 为VOCs 物种总数,p 为因子总数,x ij 为样本i 中物种j 的浓度,g ik 为第k 个因子对样本i 的贡献,f kj 为第k 个因子中物种j 的含量,u ij 为样本i 中物种j 的不确定度.PMF 模型需要输入VOCs 物种浓度数据文件和不确定度数据文件. 本研究中,若VOCs 物种体积浓度低于或等于其方法检出限,其体积浓度替换为对应方法检测限(MDL )的0.5倍,不确定度替换为MDL 的5/6;若高于MDL ,不确定度通过下式计算:U j =√(EF j ×C j )2+(0.5×MDL j )2(3)式中,U j 为物种j 的不确定度,EF j 为物种j 的误差分数(本研究中该值为20%),C j 为物种j 的体积浓度,MDL j 为物种j 的方法检出限.1.4 臭氧生成潜势臭氧生成潜势(ozone formation potential, OFP )表征不同VOCs 生成臭氧的潜能,是综合衡量VOCs 物种的反应活性对臭氧生成的指标参数. OFP 可根据最大增量反应活性(MIR )[19]计算,如式(4)所示.OFP =[VOC]×MIR (4)其中,VOC 和MIR 分别为单个VOC 组分的浓度和最大增量反应活性.2 结果与讨论(Results and discussion)2.1 臭氧及其前体物污染特征2021年兰州市O 3浓度均值变化如图1所示. 春季(3—5月)O 3浓度均值为112 µg·m −3、夏季3 期常毅等:基于超级站观测的兰州大气挥发性有机污染物特征及对臭氧形成的影响10271028环 境 化 学43 卷(6—8月)为144 µg·m−3、秋季(9—11月)为90.3 µg·m−3、冬季(12—2月)为38.2 µg·m−3. 2013—2017年,兰州市O3浓度年均值从55 µg·m−3提高到104 µg·m−3,2021年O3浓度年均值为96.1 µg·m−3,O3浓度的上升得到了抑制,但仍处于较高水平[20]. O3污染呈现典型的季节特征,兰州市夏季O3污染最严重.在NO x及VOCs等O3前体物全年排放稳定的情况下,夏季由于日照充足、紫外辐射强(图1)、光化学反应强烈,更易发生光化学污染[16]. 其中7月是O3污染最严重的月份,超标天数(>160 µg·m−3)最多,达到14 d.图 1 2021年O3及紫外辐射(UVA)月均浓度变化Fig.1 O3 and UVA mean monthly concentrations in 20212021年兰州市夏季O3、过氧乙酰硝酸酯(PAN)、VOCs和NO2日均浓度变化如图2所示. Pearson相关系数显示,PANs(r=0.56, P=0.01)、芳香烃(r=0.43, P =0.01)、烷烃(r=0.36, P =0.03)、烯烃(r=0.25, P =0.02)、NO2(r=0.28, P=0.04)与O3间呈一定的正相关,其它前体物(−0.11<r<0.05, 0.03< P<0.11)对O3浓度没有明显贡献. PAN是VOCs和NO2光化学反应生成的二次有机污染物,是光化学烟雾中的重要污染物之一[21 − 23],PAN和NO2与兰州市夏季O3有较明显的关联,说明兰州市夏季的臭氧污染与光化学反应密切相关. 不同种类VOCs与O3的相关性差异较大,芳香烃、烷烃和烯烃与O3的相关性较显著,是兰州市夏季臭氧污染的主要VOCs贡献物种. 2021年夏季长三角地区苏州臭氧平均值为137 μg·m−3,臭氧浓度对芳香烃和烯烃最为敏感,烷烃次之,与兰州近似,但污染峰值出现在6月[24]. 7、8月份长三角地区受降水影响较大,臭氧浓度下降[25],说明气候对不同地区臭氧浓度有较大影响. 关中城市群各国控站2015—2021年O3浓度评价值多年平均值为135—164 μg·m−3,主要浓度区别在冬季,关中城市冬季O3浓度均值在50—60 μg·m-3[9]. 关中城市群及周边区域冬季O3浓度升高主要与冬季NO x减排幅度高于VOCs减排幅度,区域滴定效应减弱有关,同时汾渭平原其他城市大气区域传输也有一定影响[9,26].2.2 VOCs污染特征兰州市2021年VOCs及其各组分物种(烷烃、OVOCs、卤代烃、烯烃、芳香烃、炔烃和硫化物)的月均浓度特征如图3所示. 总体上,TVOCs的月均浓度呈现秋冬季较高,春夏季较低的特征,春夏秋冬的季节平均浓度分别为72.46、66.28、135.68、121.01 µg·m−3. 兰州市VOCs月均浓度差异显著,其中12月份和6月份的VOCs月均浓度水平为最高和最低,分别达到152.06 µg·m−3和45.11 µg·m−3. 夏季臭氧污染严重,其中7月份浓度达到155 µg·m−3,然而夏季VOCs浓度水平较低,6—8月的VOCs浓度分别为45.11、63.57、90.25 µg·m−3,较其他季节低,主要是因为夏季O3的大量生成对VOCs损耗较大导致其浓度水平较低[27],另一方面,夏季温度高、光照强度强、大气边界层高、空气对流强和污染扩散条件好[28],综合导致VOCs浓度进一步下降. 而在相同的VOCs排放源和排放水平下,臭氧对其消耗较少和污染物积累条件良好的冬季则表现出最高的VOCs浓度水平.图 2 2021年夏季臭氧及其前体物浓度逐日变化Fig.2 Daily variations of ozone and its precursors in summer 2021图 3 2021年VOCs 月均浓度变化Fig.3 The monthly average concentration of VOCs in 2021兰州市2021年大气环境中VOCs 的主要组成特征为:OVOCs 和烷烃为主要的VOCs 贡献物种,其在VOCs 中的占比分别达到35.7%和30.8%,芳香烃、卤代烃、烯烃和炔烃对于大气环境VOCs 的贡献相对较少,分别为10.7%、10.0%、8.0%和4.5%. 兰州2019年大气VOCs 主要特征组分为烷烃(68%)、烯烃(19%)和芳香烃(9.5%)[29],相比于2019年,2021年OVOCs 的排放占比提升显著. 2021年兰州市各季节VOCs 组成特征中,均表现为OVOCs 和烷烃占比最高,表明机动车排放源、燃烧源和化工源等3 期常毅等:基于超级站观测的兰州大气挥发性有机污染物特征及对臭氧形成的影响10291030环 境 化 学43 卷可能为该地区VOCs的主要排放源[30 − 32]. 烯烃类占比在冬季显著升高,推测可能与冬季居民采暖燃烧源排放大量烯烃类物质有关[31]. 兰州市2021年大气环境中VOCs优势物种的年均浓度水平如图4所示,10种含量最高的VOCs物种分别为:乙醇、丙酮、二氯甲烷、乙烷、三氯乙烯、丙烷、乙烯、正丁烷、异戊烷、乙炔,其中OVOCs类2种、烷烃类4种、卤代烃类2种、烯烃类1种、炔烃类1种,其中乙醇、丙酮和二氯甲烷等物种可能与该地区化工源有关[33 − 34],待进一步分析. 在夏季臭氧高污染时期,该地区大气环境中VOCs优势组分类别与全年相似,表明该地区VOCs排放源季节差异性较小;但占比有些许差别,具体表现为OVOCs和卤代烃占比提高,分别为39.4%和17.6%,而烷烃类占比相对降低为27.4%.图 4 2021年均(左)和夏季(右)VOCs优势物种浓度Fig.4 The concentration of VOCs dominant species in annual (left) and summer (right) in 20212.3 VOCs来源解析本研究将处理后的VOCs数据输入PMF模型,经过多次模拟运行,最终解析出6个因子. 因子1中含量较高的成分为乙烷、丙烷、正丁烷、苯,煤炭燃烧产生的气体中主要为正构烷烃[35],因此将因子1标记为化石燃料燃烧源;因子2中环戊烷与正戊烷占其总量的70%以上,丙烷和戊烷是液化石油气和天然气的主要成分[36],所以将因子2标记为机动车排放源;对因子3贡献较大的成分为乙烯、乙炔、苯乙烯和1-丁烯,与有机化工和化学品的制造有关[37],因此认为因子3代表化工工艺源;因子4中2-甲基戊烷、乙烷占比较高,2-甲基戊烷是汽油挥发的示踪物[35],故将因子4标记为汽油挥发源;因子5中乙苯、苯乙烯贡献较大,乙苯、苯乙烯是油漆类物质的主要成分[38],故将因子5标记为溶剂使用源;因子6中异戊二烯含量最高,占植物排放的示踪剂[39]异戊二烯总量的83.1%,因此将因子6归于天然源. 可见,兰州市VOCs的主要来源有化石燃料燃烧源、机动车排放源、化工工艺源、汽油挥发源、溶剂使用源和天然源.兰州市2021年各排放源对VOCs的贡献如图5所示. VOCs的天然源贡献占比较小(4.5%),主要来自于人为源. 机动车排放源占比最高,达到27.1%,其次为化石燃料燃烧源(23.8%)和化工工艺源(17.9%),汽油挥发源(16%)与溶剂使用源(10.7%)占比相对较低. 周茜的研究揭示[10],2017年兰州VOCs来源主要有8类:混合工业过程-煤炭(13.5%),二次形成(13.2%),混合工业过程-燃油(11.8%),住宅生物燃料和废物处理(13.8%),溶剂使用(10.1%),汽车尾气(11.8%),生物来源(13.8%)和生物质燃烧(12.0%). 对比发现,2021年兰州市机动车排放源占比相对2017年大幅上升,化石燃料燃烧排放保持较高水平. 兰州市人口密集,车辆保有量大,石化和冶炼行业发达,因此机动车排放源与化石燃料燃烧源贡献较大,石化行业排放整治后,重点应集中在机动车管理和冶炼行业的治理上.苯系物对人体健康影响较大,同时不同种类苯系物的比值也可指示不同VOCs排放源的贡献. 兰州市2021年日均苯系物排放特征如图6所示,乙苯相对排放量在0.5以下,甲苯相对排放量集中在0.4—0.8之间,苯相对排放量在0.6以上,乙苯排放较少,主要以苯和甲苯排放为主. 苯和甲苯的比值在0.5附近时表明机动车尾气排放污染为主[40 − 41];当苯/甲苯大于0.8时,主要受到燃煤等影响[42 − 43],而当苯/甲苯小于0.5时,主要受溶剂使用等影响[42]. 兰州市苯/甲苯的值基本处于0.6以上,属于机动车尾气与燃煤混合污染,苯系物治理应着重在机动车管理与工业燃煤排放上.3 期常毅等:基于超级站观测的兰州大气挥发性有机污染物特征及对臭氧形成的影响1031图 5 2021年VOCs来源解析Fig.5 The source appointment of VOCs in 2021图 6 苯系物来源特征Fig.6 The origin characteristics of benzene series兰州市夏季臭氧污染阶段VOCs贡献源如图7所示,机动车排放源贡献最高(6月31.6%,7月30.2%,8月31.0%),其次为化石燃料燃烧源(6月20.1%,7月18.1%,8月20.8%)、汽油挥发源(6月16.1%,7月17.5%,8月13.8%)和化工工艺源(6月13.0%,7月10.7%,8月14.3%),溶剂使用源(6月10.6%,7月13.2%,8月12.2%)和天然源(6月8.6%,7月10.4%,8月7.8%)贡献较少. 机动车排放源、天然源、溶剂使用源和化石燃料源的占比变化较小,极数小于2.7%,而汽油挥发源和化工工艺源占比变化相对较大,极数分别为3.7%和3.6%. 石化和化工行业排放量随时间变化波动较大,建立相关行业高时间分辨率的VOCs排放监测和清单对进一步治理汽油挥发源和化工工艺源VOCs排放有重要意义.2.4 VOCs对臭氧生成潜势的贡献大气中不同VOCs物种的反应活性由于其化学结构的不同,对臭氧生成的影响也存在较大差异,因此采用MIR来计算VOCs的OFP,以此来评估VOCs不同组分对于臭氧生成的不同贡献. 本研究计算了全年年均和夏季阶段不同VOCs的OFP贡献比例,结果如图8所示. 烯烃的浓度水平在全年和夏季VOCs中均较低(分别为8%和6%),然而烯烃是大气环境中活性较强的组分[44],因此其对臭氧生成的影响贡献最大,分别达到了47.2%和40.7%. 芳香烃具有反应活性强的特点,在兰州大气环境中对O3的形成贡献仅次于烯烃,年均贡献和夏季贡献比例分别为23.9%和21.8%. 而在大气环境中浓度水平占比最大的OVOCs和烷烃,由于其相对较弱的化学反应活性,对于臭氧的形成贡献相对较低. 兰州市2021年年均OFP贡献较大的VOCs物种类别和夏季阶段相似,分别为乙烯、丙烯、甲苯、醋酸乙烯1032环 境 化 学43 卷酯、丙烯醛、异戊烷、正丁烷、反-2-丁烯、1.3-丁二烯、邻二甲苯,其中,乙烯、丙烯、甲苯对臭氧生成潜势贡献较大,应重点关注.图 7 兰州市夏季臭氧污染阶段VOCs来源解析Fig.7 The source appointment of VOCs in summer ozone pollution stage in Lanzhou图 8 2021年均(左)和夏季(右)VOCs的OFP贡献比例Fig.8 The annual (left) and summer (right) OFP contribution ratio of VOCs in 20213 结论(Conclusion)(1)兰州市2021年夏季O3污染严重,其中7月份超标天数(>160 µg·m−3)达到14 d,PANs、VOCs 和NO2与兰州市夏季O3有正相关关系,表明兰州市夏季的臭氧污染主要影响因素为光化学反应.(2)兰州市2021年TVOCs的月均浓度呈现秋冬季节较高,春夏季节较低的趋势,其中12月份和6月份的VOCs月均浓度水平为最高和最低,分别达到152.06 µg·m−3和45.11 µg·m−3. VOCs组成的季节性差异不大,占主要贡献的VOCs物种为OVOCs和烷烃.(3)源解析结果表明,兰州市VOCs主要来自于人为源排放,其中主要来自机动车排放源,贡献达到27.1%,化石燃料燃烧源(23.8%)和化工工艺源(17.9%)次之. 另外,该地区的苯系物排放主要受到机动车和工业燃煤排放影响.(4)烯烃和芳香烃为大气中影响臭氧生成的主要VOCs物种,其对年均OFP的贡献分别达到47.2%和23.9%,烷烃虽在大气中浓度水平较高但对臭氧生成影响较小. 其中,乙烯、丙烯、甲苯对臭氧生成潜势贡献较大,应为VOCs排放重点监测组分.参考文献(References)KOUNTOURIOTIS A, ALEIFERIS P G, CHARALAMBIDES A G. Numerical investigation of VOC levels in the area of petrol [ 1 ]stations[J]. Science of the Total Environment, 2014, 470/471: 1205-1224.[ 2 ]SHI X R, ZHENG Y X, LEI Y, et al. Air quality benefits of achieving carbon neutrality in China[J]. Science of the Total Environment,3 期常毅等:基于超级站观测的兰州大气挥发性有机污染物特征及对臭氧形成的影响10332021, 795: 148784.[ 3 ]LI K, JACOB D J, LIAO H, et al. Ozone pollution in the North China Plain spreading into the late-winter haze season[J]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(10): e2015797118.[ 4 ]LING Z H, GUO H, CHENG H R, et al. 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水与正十六烷中有机物的Henry定律常数
Air-Liquid Partition Coefficient for a Diverse Set of Organic Compounds:Henry’s Law Constant in Water and HexadecaneS A I D H.H I L A L,*,†S A R A V A N A R A J N.A Y Y A M P A L A Y A M,‡A N D L I O N E L A.C A R R E I R A‡Ecosystems Research Division,National Exposure Research Laboratory,U.S.Environmental Protection Agency,960 College Station Road,Athens,Georgia30605,and Department of Chemistry,University of Georgia,Athens,Georgia30602Received February26,2008.Revised manuscript received October11,2008.Accepted October14,2008.The SPARC vapor pressure and activity coefficient models were coupled to successfully estimate Henry’s Law Constant (HLC)in water and in hexadecane for a wide range of organic compoundswithoutmodificationto,oradditionalparametrization of,either SPARC model.The vapor pressure model quantifies the solute-solute intermolecular interactions in the pure liquid phase,whereas the activity coefficient model quantifies the solute-solvent and solvent-solvent(in addition to the solute-solute)interactions upon placing solute,i,in solvent,j. These intermolecular interactions are factored into dispersion, induction,dipole-dipole,andH-bondingcomponentsuponmoving a solute molecule from the gas to the liquid phase.The SPARC HLC calculator so produced was tested and validated on the largest experimental HLC data set to date:1356 organic solutes,spanning a wide range of functional groups, dipolaritiesandH-bondingcapabilities,suchasPAHs,PCBs,VOCs, amides,pesticides,and pharmaceuticals.The rms deviation errors for the calculated versus experimental log HLCs for1222 compounds in water and563in hexadecane were0.456and 0.192log[(mol/L)/(mol/L)]units,respectively,spanning a range of more than13and20log HLC dimensionless units for the compounds in water and hexadecane,respectively.The SPARC calculator web version is available for public use,free of charge,and can be accessed at .IntroductionHenry’s Law Constant(air/liquid partition coefficient)is one of the key properties used to describe the behavior of organic chemical pollutants in the environment.HLC quantifies the relative tendency of a compound to exist in the vapor state as opposed to remaining dissolved in a specified solvent. Organic compounds with low HLC values tend to accumulate at equilibrium in the liquid phase,although those with high HLC values will partition more to the gas phase.Partitioning of organic solutes in air-water binary systems is an important phenomenon in many scientific and engineering studies relative to chemical production and environmental assess-ment.Chemical engineering process design,environmental remediation alternatives assessment,and simulation of the fate and transport of organic chemicals in the ecosphere are areas where HLC data are crucial inputs.Specifically, volatilization from ambient surface water,wash-out and rain-out from the lower troposphere,and evaporation from contaminated aqueous media are all pollutant phase transfer processes where the behavioral differences among chemical compounds are driven by differences in their HLCs.In spite of the importance of air-water HLCs,experi-mental data are not readily available for many chemicals. Reliable measurements of these HLCs are often difficult and costly due to adsorption of minute amounts of solute on the walls of analytical apparatus and poor detection limits relative to the low water concentrations of very hydrophobic compounds.Therefore,there are fewer data available for HLC than,for example,liquid-liquid partition coefficients, such as the octanol/water partition coefficient(K ow).In a critical review of published water HLC data,Mackay and Shiu reported only40experimentally measured data for167 organic compounds of environmental concern(1).Through-out the literature,water HLC data have been reported for fewer than1300organic chemicals out of the more than 70000in current use(2-4).Additional measurements are becoming available,but at a fraction of the rate at which new chemicals,whose HLCs are needed,are being developed and introduced into commerce.Even when measured HLC values are available,significant discrepancies exist among those from different sources.For example,Nirmalakhandan found considerable differences among reported experimental water HLC values for65common organic solvents from different sources;the average factor of variability was2.56 (5).Lack of accurate water HLC values has caused difficulty in assessing the environmental behavior of many organic compounds(1-7).The objective of this study was to use the coupled SPARC vapor pressure and activity coefficient models to estimate HLCs for molecules with a wide range of functional groups, dipolarities,and H-bonding capabilities in nonpolar and polar liquid phases strictly from molecular structure,without modification to,or additional parametrization of,any of the SPARC physical processes submodels.Because HLC is directly related to solute-solvent interactions,water and hexadecane were chosen as the solvents in this study for two reasons:(1) Hexadecane is nonpolar,so HLC is driven by only dispersion and induction forces,whereas water is strongly polar with strong H-bonding such that solute-water interactions are driven by dispersion,induction,dipole-dipole,and H-bonding forces.(2)These two solvents have the most available measured and relatively most accurate HLC data.Training and Validating Sets.Only72and54polar and nonpolar compounds with measured HLCs in water and hexadecane,respectively,were included in the overall SPARC training set that contains about3900compound properties. SPARC physical property models have been trained on vapor pressure(P i o),boiling point(BP),diffusion coefficient,heat of vaporization(∆H vap),activity coefficient(γ∞ij),solubility, GC retention time,and partition coefficient(8,9)as functions of temperature,pressure,BP,and solvent.The SPARC P i o model has been validated against all the measured vapor pressure data available at25°C.In addition,the P i o model was tested on BPs spanning a range of more than800°C;and on∆H vap data points at25°C and at the BPs(9)as shown in Table1.Theγ∞ij model was previously validated for2647 organic compounds in163solvents and for the solubilities*Corresponding author E-mail:hilal.said@;Phone:706-355-8210†National Exposure Research Laboratory,U.S.EnvironmentalProtection Agency.‡University of Georgia.Environ.Sci.Technol.XXXX,xxx,000–00010.1021/es8005783CCC:$40.75 XXXX American Chemical Society VOL.xxx,NO.xx,XXXX/ENVIRONMENTAL SCIENCE&TECHNOLOGY9A Published on Web 11/11/2008and distribution coefficients of organic compounds(8)as shown in Table1.The HLC Calculator developed was tested on the largest experimental HLC data set to date,1356organic compounds. There were1222and563measured log HLC values for water and hexadecane,respectively,spanning a range of more than 13and20log HLC dimensionless units in water and in hexadecane,respectively.The data set compounds contain nonpolar carbons,halogens,nitrogen(amines,in-rings n, nitriles,nitrates,nitrosos,nitros),oxygen(aldehydes,ketones, esters,ethers,alcohols,carboxylic acids),and sulfur(thios and sulfides);and include imides,amides,PAHs,PCBs,VOCs, and pesticides,spanning a wide class of organic molecular structures.Henry’s Law Constant.HLC is defined as the ratio of a compound’s concentration in the gas phase to that in the liquid phase as a function of pressure at equilibrium(10).A plot of the solubility or concentration of an organic com-pound in the liquid phase expressed as mole fraction, ,versus the partial pressure of the compound in the gaseous phase, P i,is usually linear at low liquid concentrations(at least for compounds that are not subject to significant association or dissociation in the gaseous or in the liquid phase).At equilibrium,HLC can be expressed asH i)P iC w(1)where P i is the partial pressure of the solute i(in atm)in the gaseous phase and C w is the equilibrium molar concentration of the compound i(in mol/L)in solution(say water)at infinite dilution.For an ideal solution,where all molecules interact with their neighbors as though they were their own kind,the partial vapor pressure of compound i in the gas phase is given by Raoult’s Law(10)asP i)P i o w(2) where w is the solute i mole fraction concentration in the liquid and P i o is the vapor pressure of the pure liquid i.In real solutions,the solubility of a solute i in a solvent is determined by solute i’s activity coefficient in the solvent that can be viewed as a“correction factor”to Raoult’s Law. Hence,for real solutions(nonideal mixtures)P i)P i o wγw(3) whereγw is the Raoult’s Law-based activity coefficient of solute i in the solvent(say water).When the solution becomes saturated with solute i,the equilibrium partial vapor pressure of solute,P i,in air above the saturated solution is equal to the vapor pressure of pure solute i liquid,P i o,at the same temperature.In this later case,γij satd ij satd is unity and the saturated solvent(say water)solubility, w satd,is thus1/γw satd. Under infinite dilution conditionsP i∞)P i o w∞γw∞(4) whereγw∞is the infinite dilution Raoult’s Law-based activity coefficient of solute i in the solvent,e.g.,water,and according to eqs2and4,the HLC definition becomesH i)P i oγw∞ w∞w∞)Pioγw∞(5)SPARC was used to estimate HLC byfirst estimating the vapor pressure of the pure solute,P i o,and then the infinite dilution activity coefficient,γ∞ij,of the solute i in solvent j.Both models are based on quantifying the relevant intermolecular inter-actions in the pure liquid phase(P i o)and in solution(γ∞ij). These intermolecular interactions are factored into disper-sion,induction,dipole-dipole,and H-bonding components upon moving a solute molecule from the gas to the liquid phase(8,9).Each interaction mechanism is quantified using four molecular descriptors,molecular volume,molecular polarizability,microscopic bond dipole,and H-bonding parameters that,in turn,are calculated from molecular structure using SPARC(8,9).Vapor Pressure Model.SPARC expresses the vapor pressure,P i o,of a pure solute as a function of all the intermolecular interaction mechanisms and the entropy of expansion aslog P i o)-[∆G ii(Interaction)+∆G ii(Entropy)2.303RT](6) where R and T are the ideal gas law constant and temperature in degrees Kelvin(K),respectively.∆G ii(Interaction)is the total free energy change of the solute-solute intermolecular interactions;it quantifies the difference in the energy of all the intermolecular interactions in the gaseous versus those in the liquid phase(9).∆G ii(Entropy)is the entropy of expansion and can be expressed as∆Gii(Entropy)-2.303RT)log T+C(7)where C is a constant independent of molecular geometry, structure,or class of the solute i compound.At temperature T)25°C,we have established that∆G ii(Entropy)/-2.303RT )log T+C)2.054(unitless),and represents the change in the entropy contribution to the free energy change associated with the volume change of the solute molecule going from the liquid density to the ideal gas phase(9).Activity Coefficient Model.The infinite dilution activity coefficient,γij∞,model describes the solute-solvent and solvent-solvent(in addition to the solute-solute)intermo-TABLE1.Statistical Performance of Previous Validation of SPARC’s Vapor Pressure and Activity Coefficient Calculators property no.nonpolar no.polar total rms R2conditions vapor pressure a(log atm)4473007470.150.994T)25°Cboiling point a(°C)215018554005 5.710.999P i o)0.05-1520Torr heat of vaporization a(log(Kcal/mol))84142212630.410.990T)25°C&BP diffusion coefficient a(cm2/s)16921080.0030.994N/Aactivity coefficient b(log MF)1401124626470.2720.953T)25°C,163solv solubility b,c(log MF)1245837070.4870.982T)25°C,20solv distribution coefficient b(log(mol/L)/(mol/L))1055936980.440.978T)25°C,6solvGC retention time(Kovtas)1561316910.80.998T)25°C,squalane 537612914.20.992T)25°C,apolane87a Reference9.b Reference8.c In single solvent,for binary mixture solvent,see ref8;BP,boiling point;no.,number of solute compounds in the test set;MF,mole fraction;T,temperature in degrees Celsius,°C;P i o,vapor pressure;solv,the number of solvent compounds in the test set;R2:squared correlation coefficient;rms:root mean square.B9ENVIRONMENTAL SCIENCE&TECHNOLOGY/VOL.xxx,NO.xx,XXXXlecular interactions upon placing solute,i,in solvent,j.SPARC expresses the infinite dilution activity coefficient as a function of all the intermolecular interaction and entropy changes as logγij∞)-[∆G ij/(Interaction)+∆G ij(Entropy)2.303RT](8) where∆G ij/(Interaction)is the net free energy change involved in the solute-solute(the energy required to break all ii solute-solute interactions in the pure liquid phase), solute-solvent(the energy regained by allowing solute to interact with solvent),and solvent-solvent(the cavity energy required to make room for the solute molecules in the solvent) intermolecular interactions(8).∆G ij(Entropy)is the Flory-Huggins(11,12)excess entropy of mixing contribution in the liquid phase for placing a solute i molecule in the solvent, modified for H-bonding(HB)interactions,and is given by-∆Gij(Entropy)2.303RT )-logV iV j+(V i V j-1)2.30310∑(δG ij-∆G ii)HB X HB(9)where V i and V j are the molecular volumes of the solute i and the solvent j,respectively.The original Flory-Huggins(FH) term is the numerator of eq9.Because of potential differences in the size and shape of the solute and solvent molecules in the solution mixture,the FH term is essential in calculating many physical properties in solution such as theγij∞,solubility and HLC.From eq9,we see that when the solute and solvent have the same molecular volume,the FH term goes to zero. On the other hand,when the solute molecular volume is small and the solvent molecular volume is large, e.g., hexadecane,then the logγij∞can be negative because of the large FH contribution.∆G ii andδG ij in eq9are the solute-solute and solute-solvent HB interaction energy changes,respectively.To-gether,they describe the net differential mixing energy of an “isolated”solute molecule“i”placed into solvent“j”due to HB interactions;X HB is the“reduction susceptibility”of the FH term that can be inferred indirectly fromγij∞measure-ments.X HB quantifies the reduction of the FH term due toHB interactive orienting forces that reduce the randomness of placement.Note that∆G ii<0(the energy needed to break or disrupt the solute-solute molecules)andδG ij>0(the energy regained from placing a solute in a solvent)(8).For nonpolar compounds,the dominator is one,and∆G ij(En-tropy)becomes the original FH term.Results and DiscussionSPARC-calculated vapor pressures P i o of pure solute com-pounds and their infinite dilution activity coefficientsγij∞in water and hexadecane were used successfully via eq5to estimate the corresponding HLCs without modification to, or additional parametrization of any of the physical properties models involved.Thus,errors associated with the calculations of these two properties propagate to the estimated HLC values.The errors observed herein for our estimated HLCs were consistent with those previously observed in our calculated P i o andγ∞ij values for both simple and complex solute molecular structures(see Table1).The total rms deviation between the calculated and experimental values for1222compounds in water and563compounds in hexadecane were0.456and0.192log HLC[(mol/L)/(mol/ L)],respectively,as shown in Figures1and2and Table2. In general,for simple solute molecular structures,SPARC estimated the P i o andγ∞ij values(thus HLCs)to better than a factor of2as shown in the statistical performance for the C,N,O,S,and halogen compounds in Table2.For more complex molecular structures,where functional groups in a complex molecule can influence each other(e.g.,intramo-lecular H-bonding and dipole-dipole interactions),such as complicated pesticides,the SPARC calculations are within a factor of3or4,also as shown in Table2.HLC in Water versus Hexadecane.Recall that HLC is a measure of the relative affinity of a compound for its vapor phase versus its dilution in a liquid solvent phase.In the gas phase,behavior is close to ideal,so HLC depends primarily on the solute-solvent and solvent-solvent interactions in the liquid phase(note that solute-solute interaction cal-culations for the vapor and liquid phases cancel each other). The ultimate intermolecular interaction of an organic solute with water or hexadecane is a constitutive property of the solute compound,and is described in SPARC by dispersion, induction,dipole-dipole and H-bonding forces.Because organic solute-hexadecane intermolecular interactions are driven overwhelmingly by dispersion and induction forces whereas the solute-water intermolecular interactions involve all four forces,the rms deviation error for water was expected to be and was higher than that for hexadecane(more than twice),as shown in Table2.Another reason for the relatively large rms error for water is that there were more complex solute(i.e.,pharmaceuticals and complicated pesticides) measurements in the water test data set than for hexadecane. For example,the worst three outliers in the hexadecane data set were1,5-cyclooctadiene,dicyclopentadiene,and ben-zamide;the absolute deviations of their calculated values from the observed values were0.77,0.64,and0.78logHLC, FIGURE1.Observed versus SPARC-calculated log HLC at25°C in hexadecane.The calculations span a range of more than20 log Henry’s Law Constant(unitless).The rms,AVE,and R2values are0.192,0.143,and0.996log HLC(mol/L)/(mol/L),respectively.FIGURE2.Observed versus SPARC-calculated log Henry’s Law Constant in water.The HLCs span a range of more than13log Henry’s Constant(mol/L)/(mol/L).The rms,AVE,and R2values are0.456,0.317,and0.984,log HLC unitless,respectively.The outliers are:(A)chlorfluzuron,(B)fluquinconazole,(C) pentanedioic acid,(D)ancymidol,(E)methazole,(F)trichloro acetic acid,(G)trifluoro acetic acid,(H)hexafluoroethane,(I) N-methyl-N-nitroso-benzenemethanamine,(J)methyl salicylate, (K)pyrazine,2-methoxy-3-isobutyl,and(L)peroxyacetylnitrate.VOL.xxx,NO.xx,XXXX/ENVIRONMENTAL SCIENCE&TECHNOLOGY9Crespectively.On the other hand,the worst three outliers in water were methazole,fluquinconazole,and methylnitroso carbamic acid ethyl ester;the calculated HLC for each of these three compounds deviated by more than3.5log HLC. In addition,the HLC measurements in hexadecane were often of better quality than those in water;see following text.Evaluation of Calculated HLC Values.Yaffe et al. presented thefirst neural network approach to estimate HLCs of organic compounds based on two approaches;fuzzy ARTMAP and back-propagation neural networks(13).They developed a set of molecular descriptors(i.e.,molecular polarizability and dipole moment,etc.)from PM3MO-theory and a molecular connectivity index from topology as input parameters to the neural networks.Upon testing,they found that the fuzzy ARTMAP-based QSPR was superior to the back-propagation for a heterogeneous set of495organic com-pounds.Their results were impressive;the average absolute errors(AVE)for their training and their testing sets for the first approach were0.03and0.13log HLC unitless,and for the second approach were0.28and0.27log HLC unitless, respectively.Their study showed“it is possible to develop reasonably accurate QSPRs for heterogeneous organic com-pounds based on the fuzzy ART classifier and the fuzzy ARTMAP cognitive system using a set of descriptors calculated from quantum mechanics and graph theory”(13).However, the AVE of either approach was actually smaller than intralaboratory experimental error,a bit disconcerting.Dearden and Schu¨u¨rmann evaluated the predictive performance of12methods to estimate HLC in water using a test data set of700solute chemical structures.They found the overall best-performing model was the HENRYWIN software package that is based on a chemical bond contri-bution method,yielding a predictive R2of0.87and rms deviation of1.03log HLC units for the test data set(14).In a recent article,Modarresi,et al.reported that the rms deviation errors for predicted HLCs using a940solute compound test data set were0.69,0.79,and0.54log HLC units for chemical bond contribution,group contribution and the best QSPR model applied in their study,respectively (15).All940compounds that Modarresi et al.tested are in Table2,except two that were removed because SPARC does not parse those structures at the present pounds that ionize at low pH,e.g.,most of the highly substituted halogenated phenols and ortho/para nitrophenols,or those that ionize at high pH,such as those containing sp3nitrogen as a base,were moved from Table S1to Table S2in the Supporting Information,section,with the reason(s)for the move noted in the last column of Table S2.Similarly, compounds that hydrate in water,such as propanal,2-oxo-and2-propanone,1,1,1-trifluoro were also moved to TableS2(unless the measured value was corrected for hydration,such as for formaldehyde).For2-propanone,1,1,1-trifluoro,SPARC calculated the log HLC assuming no hydration.However,the effective Henry’s Law Constant(log HLC/)thatis the number reported as“experimental”can be calculatedby including SPARC’s hydration models as is shown in FigureS1in the Supporting Information.Propanal,2-oxo is acomplex molecule that both hydrates and tautomerizes toform a new species CO2HCH(OH)CH3that is ionized at pHg3.8as shown in Figure S2in the Supporting Information. In addition,other compounds were moved when we felt therewere grievous errors in measurement or when their P i o valueshad been extrapolated from very high temperature to25°C.For all of the compounds moved to Table S2in the SupportingInformation,SPARC-calculated values were not included inthe performance statistical parameters reported herein.Examination of HLC Values.The experimental HLCs usedin this study comprise values that were either(1)measureddirectly or(2)derived from reliable P i o and water solubility(S ij)data.As shown in Table S1in the Supporting Information,many of the SPARC-calculated results correspond closely tothe experimental values;however,several deviate signifi-cantly.Some deviation between the experimental andcalculated values for HLC can be attributed to incorrect S ijand/or P i o measurements.Many HLC values that have beenreported as experimental are,in fact,calculated from theratio of experimental P i o and S ij.Indeed,according to theliterature,this is the most popular method for estimatingHLC.However,this is an indirect method based on thedefinition of HLC using the ratio of the P i o of the solutecompound to its ultimate S ij in the liquid solvent phase.Theunderlying assumption in this method is that HLC is validup to the saturation solubility limit of the solute compound.If this assumption is satisfied,this method yields a true valuefor HLC.It therefore follows that the experimental errorsassociated with these two properties will be propagated intothese“so-called experimental HLC”values.The error in P i o measurement can be as large as a factorof2-3(16),as in the case of high-molecular-weight and lowP i o solutes.If the solute is solid at environmental temper-atures,and if P i o data of that solute are available at an elevatedtemperature,then extrapolation of P i o through the triple pointis necessary,with attendant loss of reliability.Most measuredP i o data are available at higher temperatures and,therefore,have to be extrapolated back to environmentally relevanttemperatures.Likewise,most experimental S ij data reportedare at25°C or more,and the nonavailability of solubility-temperature relationships often poses additional problemsTABLE2.SPARC Log HLC Calculator for Water and Hexadecane Statistical Performance awater hexadecanesets type solute typefunctional group mol.ID in Table S1no.rms AVE R2no.rms AVE R2training nonpolar N/A320.2010.1600.983320.060.050.999 polar400.4200.3100.970220.1360.0980.996 total720.330.2690.973540.080.060.998testing nonaromatic carbon1-2512160.2120.1660.9731040.2190.150.999 aromatic carbon252-331730.3040.2230.984550.1380.1030.996 nitrogen332-4791280.4220.3120.966870.2010.1560.982 oxygen480-8093150.4210.3100.9671560.1610.1190.992 sulfur810-836170.3190.2110.804240.1380.1000.993 halogen837-11452900.3910.2870.957980.1610.1180.992 mixed substituent1146-12611100.6670.4820.895380.2450.1950.966 miscellaneous b1262-1356730.8810.6630.9271N/A N/A N/A total c12220.4550.3170.9845630.1920.1430.996a This statistical performance is for compounds also used in Table S1in the Supporting Information.b Mostly pesticide compounds;c The references for the measured HLCs are given in Table S1in the Supporting Information.AVE,average absolute error;N/A,not applicable;HLC units are log(mol/L)/(mol/L);no.,number of solute compounds.D9ENVIRONMENTAL SCIENCE&TECHNOLOGY/VOL.xxx,NO.xx,XXXXin environmental applications.In addition,reliable solubility data,particularly for sparingly soluble solid hydrophobic compounds,have been difficult to obtain,and often inac-curate data have been reported(1).The interlaboratory solubility experimental errors for simple compounds(at best) are(3%,using the generator column method(17).“The precision of this method is judged to be better than3%,which is adequate for environment purposes”(1),but the intral-aboratory experimental errors are much higher even for simple structures,and can be as much as0.5log mole fraction solubility and higher for complex structures(see next section).A quick look at any of the solubility databases(18-21)shows how great the discrepancies are for experimental values from different groups,even for simple structures.In addition,Ashworth et al.(22)showed that the“so-called experimental HLC”method using the measured P i o/ measured S ij ratio will not give reliable results for HLC unless corrected for the solubility of water in the rge solubility measurement discrepancies are common.For example,Roberts(23)has cautioned about estimating HLC for highly volatile compounds that are gases at normal temperatures using solubility data.He estimated the HLC (24)based on vapor pressure-solubility data for dichlo-rodifluoromethane in aqueous solution as63.3(1.8log HLC), an overestimate of a factor of approximately6compared to the experimentally determined(true)value of11.2(1.05log HLC unitless)(24).This discrepancy was later attributed to the fact that,for gaseous solutes,S ij was often measured in equilibrium using the pure gas rather than the pure liquid. In addition,in using the measured vapor pressure and measured solubility ratio as the HLC,it is necessary to ensure that these two properties of the solute relate to the same state.For example,if both the S ij and P i o measurements are made on the same state(either subcooled liquid or solid state),then the crystal energy contributions to the S ij and P i o correctly cancel each other in estimating the HLC.Otherwise, if the P i o or S i j measurements are made on different states, then the crystal energy contribution becomes important and must be estimated using the melting point to correct for the entropy of fusion required to move the solute from the solid to the subcooled liquid state(25).PCB’s,PAH,and Complicated Pesticides.Measurements of vapor pressure and solubility for hydrophobic compounds of low volatility are very difficult,and reliable data are very scarce.In a study of PCBs,Burkhard et al.(16)suggested an average factor of error,AFE,of5.0in estimating HLC from solubility data.This AFE value of5.0corresponds to the worst-case uncertainty in the case of PCB compounds.For example, the HLC measurement for2,2′,3,3′,4,5,5′-PCB(molecule no. 1353in Table S2in the Supporting Information)is lower than the rest of the heptachlor-PCB isomers(molecules no 1129-1137in Table S1in the Supporting Information)by more than a log unit;HLC values for all heptachlor-PCB isomers should be relatively close.Similarly,the P i o values for PAHs(other than naphthalene) that are solid at25°C are obtained from higher-temperature liquid state data(1).As the number of PAH rings increases, the P i o becomes very small and less accurately known,thus the HLC values for these compounds can have a large error. Indeed,there are considerable discrepancies in the measured vapor pressures,solubilities and HLC values for the higher PAHs(1,16,26).Heller et al.(18)discuss the difficulty in obtaining correct vapor pressures and solubilities for pesticide compounds. Methods for measuring the HLCs are analytically difficult for compounds with low vapor pressures and aqueous solubili-ties,typical of many pesticides.These difficulties arise because air and water samples containing a few nanograms or less of the compound must be quantified.For example, HLC measurements in fogwater deviate substantially from the correct HLC due to dissolved organic material,surfactant and ions that may be present in fogwater(27).HLCs for pesticides,when available,usually differ by at least one or 2orders of magnitude(26,28)from the reported measured or“experimental HLC”values.For example,the solubility of alachlor is reported in the literature as242mg/L(29),148 mg/L at25°C(19),and140mg/L at23°C(30).Similarly,the reported solubility of fenthion in the sixth edition of The Pesticide Manual is54-56mg/L(31),2mg/L in the seventh edition,and other literature values are4.2and9mg/L(18). Also,Abraham et al.found the P i o for aziprotryn cited in the sixth edition of The Pesticide Manual to be incorrect,being 2orders of magnitude lower than that in the current edition (32).Consequently,they corrected the MedChem database log HLC unitless value for aziprotryn from8.34to6.35.They also corrected the Handbook of Physical Chemical Properties selected log HLC value for propazine by a factor of10(32).Generally,one or2orders of magnitude differences are observed in intralaboratory experimental measurements for S ij and P i o for many pesticides such as DDT,malathion,and parathion(18,28,33).The so-called“experimental HLC”values using the measured vapor pressure/measured solu-bility ratio method for these complex molecular structures would result in poor HLC values.Validation of the HLC Model.The SPARC modeling approach does not use any of the experimental HLC values to develop or directly influence its HLC calculations.Instead, the two fundamental physicochemical properties needed for their calculation,solute P i o andγij∞,are predicted using models that quantify the underlying intermolecular forces and interactions that drive all types of chemical behavior (dispersion,induction,dipole-dipole,H-bonding).The fundamental mechanistic models have been parametrized using a limited set of experimental data,but not data for the end-use properties that will subsequently be predicted.The aforementioned four mechanistic models were used in the SPARC modules that calculate the P i o andγij∞,as well as numerous other physical properties,including HLC.It is critical to recognize that the same four mechanistic models appear in all of the SPARC modules that predict the various end-use properties(e.g.,HLC,BP,∆H vap,partition coefficient, etc.)for which those intermolecular forces and interactions are important.Thus,any comparison of SPARC-calculated HLC values,as well as for any other physicochemical property, to an adequate experimental data set is a true model validation test;there is no training(or calibration)data set in the traditional sense used to estimate any particular property.SPARC is not limited to a particular subset of solvents or a particular temperature;any molecule that SPARC can parse and run as a solute can be declared a solvent,so essentially SPARC can calculate the HLC for any solvent-solute pair strictly from molecular structure.The SPARC calculator addresses most organic functional groups containing H,C, N,O,and halogens.It covers some,but not all,valences of S and P.The azide functional group is not addressed.In addition,the SPARC molecular parser does not recognize chiral molecules,and the only isomers addressed are cis/ trans molecules.However,SPARC allows the user to input the corresponding experimental density of the isomer to calculate a property of interest,including HLC.If the isomer density is used,then isomerization does not play a huge role in physical property prediction.In summary,HLC literature values have been shown to vary over2orders of magnitude for some complex organic compounds.Some of the deviation error in the SPARC calculations can be attributed to experimental error,while other error contributions could be modeling problems in the SPARC P i o and/orγij∞models.In general,SPARC-calculated HLC prediction accuracy decreases as the ex-VOL.xxx,NO.xx,XXXX/ENVIRONMENTAL SCIENCE&TECHNOLOGY9E。
电池充电cv阶段温升大的原因
电池充电cv阶段温升大的原因1.电池充电时,会产生一定的内阻,导致能量转化为热量而产生温升。
When the battery is charging, a certain internal resistance will be generated, leading to the conversion of energy into heat and causing temperature rise.2.充电过程中,电流在电池内部产生电阻,从而导致电池温度升高。
During the charging process, the current generates resistance inside the battery, leading to an increase in temperature.3.充电时电池内部的化学反应也会释放热量,导致电池温升。
The chemical reactions inside the battery during charging also release heat, leading to temperature rise.4.电池充电过程中,充电器也会产生一定的热量,加剧了电池温升。
The charger also generates a certain amount of heat during the battery charging process, exacerbating the temperature rise of the battery.5.温度升高会影响电池内部材料的性能,进而影响充电效率。
The temperature rise affects the performance of the materials inside the battery, thereby affecting the charging efficiency.6.长时间快速充电也会导致电池温度持续升高。
土壤碱解氮英文
土壤碱解氮英文English: Soil alkaline hydrolysis refers to the process of converting nitrogen compounds in the soil into ammonia under alkaline conditions. This process is primarily influenced by the pH level of the soil, with higher pH values increasing the rate of alkaline hydrolysis. The presence of certain alkaline or basic compounds in the soil, such as carbonates and hydroxides, can also enhance this process. Alkaline hydrolysis of nitrogen in the soil plays a significant role in nutrient cycling and availability for plants. It helps in the conversion of complex organic nitrogen compounds, such as proteins and amino acids, into simpler forms that plants can easily absorb. Additionally, the release of ammonia through alkaline hydrolysis contributes to the supply of available nitrogen for plant growth. However, excessive alkaline hydrolysis can lead to the loss of nitrogen from the soil, particularly in the form of ammonia volatilization. This can occur when the soil pH becomes too high, resulting in the conversion of ammonium ions into ammonia gas, which can escape into the atmosphere. In agricultural systems, excessive alkaline hydrolysis can negatively impact nitrogen use efficiency and may necessitate the application of nitrogen fertilizersto compensate for the losses. Therefore, understanding and managing soil alkaline hydrolysis is crucial for sustainable agriculture and soil fertility management. Strategies to mitigate excessive alkaline hydrolysis include adjusting soil pH levels through acidification, applying organic matter to promote nitrogen retention, and adopting appropriate irrigation and drainage practices to prevent the accumulation of alkaline salts.中文翻译: 土壤碱解氮是指在碱性条件下,将土壤中的氮化合物转化为氨的过程。
邻苯二甲酸酯对葡萄植株生长及物质构成的影响
15 mg / mL 时,3 个品种葡萄的叶片长、叶宽生长量
较对照减少幅度较小,无 显 著 差 异;当 PAEs 浓 度
为 50 mg / mL 时,红巴拉多和无核白鸡心葡萄的叶
长、叶宽生长量与对照差异不显著,而无核白葡萄
叶长、叶宽生长量较对照减少幅度较大,分别减少
丁酯( DBP) 、邻苯二甲酸二( 2 - 乙基己基) 酯( DE⁃
Institute of Agricultural Quality Standards and Testing Technology, Xinjiang Academy of Agricultural Sciences, Urumqi 830091,
China)
Abstract: To find out the effects of phthalates in the air on the growth and material composition of grape plants, potted
grapes were covered with a glass cover, in which methanol solution petri dishes containing DBP, DEHP and DIBP mixture were
placed to allow PAEs to be absorbed and accumulated by plants by natural volatilization. The effects of different concentrations of
试验数据进 行 统 计 分 析,单 因 素 相 关 显 著 性 检 验
采用 LSD( least significant difference) 法。
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Abstract.Selenium (Se)removal from polluted waters and soils is especially complicated and highly expensive.Phytoremediation has been suggested as a low-cost,e cient technology for Se removal.Plants remove Se by uptake and accumulation in their tissues,and by volatilization into the atmosphere as a harmless gas.Unraveling the mechanisms of Se uptake and volatiliza-tion in plants may lead to ways of increasing the e ciency of the phytoremediation process.The objec-tives of this study were:(i)to determine the e ect of di erent Se forms in the root substrate on the capacity of some plant species to take up and volatilize Se;(ii)to determine the chemical species of Se in di erent plant parts after the plants were supplied with various forms of Se;and (iii)to determine the in¯uence of increasing sulfate levels on plant uptake,translocation,and vola-tilization of di erent Se species.Plants of broccoli (Brassica oleracea var.botrytis L.),Indian mustard (Brassica juncea L.),sugarbeet (Beta vulgaris L.)and rice (Oryza sativa L.)were grown hydroponically in growth chambers and treated for 1week with 20l M Se as Na 2SeO 4,Na 2SeO 3or L -selenomethionine (SeMeth)and increasing sulfate levels.The data show that shoots of SeO 4-supplied plants accumulated the greatest amount of Se,followed by those supplied with SeMeth then SeO 3.In roots,the highest Se concentrations were attained when SeMeth was supplied,followed by SeO 3,then SeO 4.The rate of Se volatilization by plants followed the same pattern as that of Se accumulation in roots,but the di erences were greater.Speciation analysis (X-ray absorption spectroscopy)showed that most of the Se taken up by SeO 4-supplied plants remained unchanged,whereas plants supplied with SeO 3or SeMeth contained only SeMeth-like species.Increasing the sulfate level from 0.25mM to 10mMinhibited SeO 3and SeMeth uptake by 33%and 15±25%,respectively,as compared to an inhibition of 90%of SeO 4uptake.Similar results were observed with regard to sulfate e ects on volatilization.We conclude that reduction from SeO 4to SeO 3appears to be a rate-limiting step in the production of volatile Se compounds by plants.Inhibitory e ects of sulfate on the uptake and volatilization of Se may be reduced substantially if Se is supplied as,or converted to,SeO 3and/or SeMeth rather than SeO 4.Key words:Ion uptake ±Selenium uptake ±Selenomethionine ±Speciation (selenium)±Sulfate ±Volatilization (selenium)IntroductionSelenium (Se),a required trace element in the diet of animals and humans,is poisonous to animals,humans and plants if ingested in relatively high concentrations.Recently,after the discovery of Se poisoning to wildlife in California,much attention has been focused on developing ways for the cleanup of Se-contaminated soils and waters and the prevention of future build-up of Se in the environment.Phytoremediation has been suggested as a low-cost,environmentally friendly,and highly e cient approach for the cleanup of Se pollution (Terry and Zayed 1998).Plants can scavenge Se from a large volume of soil and bioconcentrate it within their relatively small biomass,which can then be harvested and removed totally from the contaminated site.In addition,plants have the capacity to remove Se from the contaminated rooting medium by metabolizing it into a non-toxic volatile gas,e.g.dimethyl selenide (DMSe)(Lewis 1971;Terry and Zayed 1994).Among other factors,the rates of Se uptake and volatilization are governed largely by the chemical form of Se present in the root substrate.Plants take up Se as selenate (SeO 4),selenite (SeO 3),and/or organic Se.MostAbbreviations:SeMeth L -selenomethionine;XAS X-ray absorption spectroscopyCorrespondence to :N.Terry;E-mail:nterry@;Tel./Fax:1(510)6423510Planta (1998)206:284±292Accumulation and volatilization of di erent chemical species of selenium by plantsAdel Zayed,C.Mel Lytle,Norman TerryDepartment of Plant and Microbial Biology,111Koshland Hall,University of California,Berkeley,CA 94720±3102,USA Received:27February 1998/Accepted:30March 1998research regarding the availability of di erent forms of Se for plant uptake and accumulation has focused on the comparison among inorganic chemical species of Se with special emphasis on the two oxidized forms:SeO4and anic forms of Se may be more readily available to plant uptake and volatilization than inorganic forms (Williams and Mayland1992;Verkleij and MacNaeidhe 1992;Zijian et al.1991).Research on Se volatilization by microorganisms showed that microbial volatilization could be100-fold higher from L-selenomethionine (SeMeth)than from inorganic Se(Reamer and Zoller 1980).Unfortunately,there are very few studies available showing how plant uptake and volatilization of Se is in¯uenced by its chemical form.In the present work,we compared uptake and volatilization of three Se forms, namely SeO4,SeO3and SeMeth,in four di erent plant species,namely broccoli,Indian mustard,sugarbeet and rice.The objective of the research was to investigate the e ect of the presence of di erent Se forms in the root substrate on the capacity of a variety of plant species to take up and volatilize Se.This information is vital in order to elucidate the physiological basis of Se uptake and volatilization in plants.Another objective of our study was to determine the chemical species of Se in di erent plant parts after being supplied with various forms of Se.Once Se is taken up by plant roots in a particular chemical form,whether organic or inorganic,it undergoes certain metabolic changes that determine the®nal product as well as its translocation and accumulation in di erent plant tis-sues.If these changes are identi®ed,it will be possible to determine the key-limiting steps in the Se metabolic pathway,which then can be manipulated by modern molecular biological techniques.Several researchers have tried to devise tools for the fractionation and speciation of Se in plant tissues(Asher et al.1977; Gissel-Nielsen1979,1987).Most of these attempts involved either indirect measurements of the di erent forms of Se or treatments that may cause chemical changes in Se species.Synchrotron-based X-ray absorp-tion spectroscopy(XAS)techniques on the other hand, allow for direct measurement of the chemical species in vivo.This technique has recently been used in the environmental speciation of contaminants in various ecological samples,including waters,soils,and plants (Pickering et al.1995;Salt et al.1995,1997;Kra mer et al. 1996;Lytle et al.1996;Manceau and Gallup1997). Chemical speciation of Se in plant tissues using XAS techniques is extremely speci®c so that once adjusted it measures only Se with no interference from any other ing this technique,we were able to accu-rately determine the Se species that accumulated inside plant tissues in the forms in which it is actually present in the intact and functioning plant.The third objective of our study was to determine the in¯uence of increasing sulfate supply levels on the uptake,translocation,and volatilization of Se by plants supplied with Se as SeO4,SeO3,or SeMeth.The rate of Se uptake and volatilization depends partly on the concentrations of competing ions in the root medium.Earlier research showed that SeO4uptake and volatil-ization by broccoli plants were inhibited as the sulfate level increased in the growth medium(Zayed and Terry 1992,1994).Since high levels of Se and S naturally coexist in most seleniferous soils,it is necessary to understand the e ects of high sulfate levels on the uptake and volatilization of Se from various chemical forms of Se that may be found in most soils,e.g.SeO4, SeO3,and SeMeth.This will help develop more-e cient phytoremediation techniques suitable for areas where sulfate levels are high.For instance,if it is found that SeO3or SeMeth uptake and volatilization are consider-ably less a ected by the higher sulfate levels than SeO4, then chemical,physical,or biological treatments may be used to change the Se form so that higher rates of uptake and/or volatilization can be achieved.Unfortunately, there are no data available to show how the changes in sulfate levels will a ect SeMeth or SeO3uptake and volatilization by plants.Materials and methodsPlant culture.Plant species used in this study include broccoli (Brassica oleracea var.botrytis L.),Indian mustard(Brassica juncea L.),sugarbeet(Beta vulgaris L.),and rice(Oryza sativa L.).All plants were cultured hydroponically in growth chambers at25°C with an irradiance of500l mol m A2s A1photosynthetic photon¯ux supplied over a16-h photoperiod.Seeds were sown in sand for two weeks during which time they were irrigated with half-strength Hoagland's solution.Germinating seedlings of similar size were transplanted and allowed to grow for another two weeks in half-strength Hoagland's solution.Selenium(20l M)was then added as Na2SeO4,Na2SeO3(Sigma Chemical Co.),or SeMeth(CalBio-chem-Novabiochem Intl.)to the nutrient solution.To test the e ect of increasing sulfate concentrations on Se uptake and volatiliza-tion,a group of broccoli plants was transferred to a modi®ed half-Hoagland's solution supplied with MgSO4in three di erent concentrations,i.e.0.25,1.0,10.0mM.For each sulfate treatment, Se(20l M)was added to the nutrient solution as Na2SeO4, Na2SeO3,or SeMeth.All the plants were allowed to grow for one more week before Se volatilization was measured.Determination of the rate of Se volatilization and analysis of Se in plant materials.One week following the start of Se treatment, plants were transferred to Plexiglas chambers and the rate of Se volatilization was determined for roots and shoots separately as described by Zayed and Terry(1994).For each treatment of each plant species,we obtained four individual volatilization measure-ments for roots and two measurements for shoots,with each shoot volatilization measurement representing two individual plants. After determining volatilization rates,plant shoots and roots were removed from the collection chambers,washed with running tap water,and oven-dried at70°C for48h.Dry plant tissues were digested and prepared for Se analyses using the HNO3/HClO4 procedure according to Mikkelsen(1987).Total Se in the plant extract was then measured using the atomic absorption/hydride generation method following Varian's procedure for the VGA-76 vapor generator.Speciation of Se in plant tissues by XAS analysis.To perform XAS analysis on Se in plant tissues,Se-treated broccoli plants were harvested,separated into leaf and root tissues,rinsed in deionized water,frozen in liquid nitrogen,ground to a®ne texture,and stored at A80°C.Selenium K-edge analyses of plant tissues were completed on Beam Line4-1of the Stanford Synchrotron Radiation Laboratory using an Si(111)double-crystal mono-chromator with a 1-mm entrance slit.Frozen leaf and root tissues were placed in a sample chamber at a 45°angle to the X-ray beam.X-ray ¯uorescence of Se in plant tissues was obtained by a series of replicate scans (3±15)dependent on Se concentration.XAS spectra were also collected for Se reference compounds.Background subtraction and normalization were carried out according to established procedures (Koningsberger and Prins 1988).ResultsSelenium concentrations in shoots and roots.Selenium accumulation was determined in shoots and roots of Indian mustard,broccoli,sugarbeet,and rice plants grown in nutrient solutions containing 20l M Se as SeO 4,SeO 3,or SeMeth (Fig.1A,B).Maximum Se levels (366±550mg kg A 1DW)in shoots of the di erent plant species were attained when Se was supplied as SeO 4,except for sugarbeet,where maximum Se levels (405mg kg A 1)were reached when Se was supplied as SeMeth (Fig.1A).In all species,shoots of SeO 3-supplied plants attained the least Se concentrations (<73mg kg A 1)compared to SeO 4or SeMeth-supplied plants.In roots of all plants,Se was bioaccumulated to the highest levels when they were supplied with SeMeth (655±2197mg kg A 1),followed by those supplied with SeO 3(230±690mg kg A 1),then those supplied with SeO 4(78±169mg kg A 1)(Fig.1B).Generally,Se transport from root to shoot was highly dependent on the chemical form of the Se supplied(Table 1).The Shoot/root ratio of total Se content in all plant species studied ranged from 0.6to 1for plants supplied with SeMeth and was less than 0.5for those supplied with SeO 3,while this ratio ranged from 1.4for rice to 17.2for Indian mustard when SeO 4was the form of supplied Se.Maximum Se accumulation per plant (shoot Se content +root Se content)was reached in plants supplied with SeO 4in broccoli and Indian mustard,while in sugarbeet and rice it was observed for plants supplied with SeMeth (Table 1).Selenite was the least accumulated form of Se in plant tissues.Of these plant species studied,Indian mustard was thebestTable 1.Ratio of shoot Se content to root Se content and total plant uptake of Se in Indian mustard,broccoli,sugarbeet and rice as in¯uenced by the chemical form of Se supplied Chemical form of Se suppliedBroccoliIndian mustard SugarbeetRiceSelenium shoot/root ratio Selenate 9.5517.2 6.121.44Selenite 0.470.300.240.40SeMeth 0.910.98NA a 0.58Total Se uptake (mg plant A 1)Selenate2.588.65 1.400.04Selenite 0.60 2.580.490.08SeMeth 2.005.555.140.38aNot availableFig.1A,B.Selenium concentrations in shoots (A )and roots (B )of Indian mustard,broccoli,rice,and sugarbeet plants supplied with 20l M Se as selenate,selenite,or SeMeth.Error bars indicate SD.NA not availableat accumulating Se in tissues regardless of the chemical form of supplied Se (Table 1).Increasing sulfate supply in the growth medium from 0.25mM to 10mM resulted in a progressive inhibition in SeO 4uptake,while it caused little or no e ect on SeO 3and SeMeth uptake,as judged by shoot and root Se concentrations (Fig.2).In SeO 4-supplied plants,in-creasing sulfate supply from 0.25mM to 1mM and 10mM decreased Se concentrations by 47%and 93%in shoots,respectively,and by 72%and 90%in roots,respectively.Plants supplied with SeO 3or SeMeth exhibited no signi®cant changes in Se concentrations with the increase in sulfate levels,except for roots of plants supplied with SeO 3,which exhibited a lower uptake at higher sulfate supply levels (33%lower uptake at both 1mM and 10mM compared to 0.25mM;Fig.2).In general,the increase in sulfate supply had amuch stronger inhibitory e ect on SeO 4uptake and accumulation than on that of SeO 3or SeMeth,especially when the sulfate level increased from 1to 10mM (Fig.2;Table 2).Rate of Se volatilization.The rates of Se volatilization by shoots and roots were measured for four di erent plant species supplied with 20l M Se as SeO 4,SeO 3,or SeMeth.The data show that for all four plant species the rate of Se volatilization was greatest when plants were supplied with Se as SeMeth and least when they were supplied with SeO 4(Fig.3).Selenium volatilization by shoots of broccoli,Indian mustard,sugarbeet,and rice plants supplied with SeMeth was 38-,98-,48-,and 15-fold higher than those supplied with SeO 4,respectively,and 25-,188-,63-,and 16-fold higher than those supplied with SeO 3,respectively.In roots,the rates of Se volatilization of broccoli,Indian mustard,sugarbeet,and rice plants supplied with SeMeth was 126,83,89,and 36times higher than those rates of plants supplied with SeO 4,respectively,and 66,51,73,and 20times higher than those of plants supplied with SeO 3,respec-tively.Roots also volatilized Se slightly faster (21±90%faster)from SeO 3than from SeO 4in all plant species studied (Fig.3).Root volatilization of Se from SeO 4,SeO 3and SeMeth was 5±14,8±30,8±48times faster than that of shoots,respectively,in the di erent plant species studied.Increasing the sulfate level in the growth medium from 0.25mM to 1mM resulted in a substantial reduction (>5-fold decrease)in the rate of Se volatil-ization by SeO 4-supplied broccoli plants.Further reduc-tion in the Se volatilization rate occurred with the increase in sulfate supply levels from 1mM to 10mM,but it was much less pronounced (Fig.4).In SeO 3-supplied plants,an increasing level of sulfate supply resulted in a slight inhibition in the rate of Se volatil-ization by roots,but had no signi®cant e ect on shoot Se volatilization rate.Rates of Se volatilization by SeMeth-supplied plants showed an inconsistent response to the changes in sulfate level (Fig.4).The data show that as the sulfate level increased from 0.25mM to 10mM the ratio of Se volatilization rate of SeO 3-supplied broccoli plants to that of SeO 4-supplied plants (SeO 3/SeO 4volatilization ratio)increased from 0.42to 3.26in shoots and from 0.64to 2.65in roots (Table 2).Similarly,the SeMeth/SeO 4volatilizationFig.2.In¯uence of increasing sulfate supply on Se concentrations in roots and shoots of broccoli plants supplied with 20l M Se supplied as sodium selenate (Selenate ),sodium selenite (Selenite ),or L -selenomethionine (Se-Met ).Error bars indicate SDTable 2.In¯uence of increasing sulfate levels in the nutrient solution on the Se concentration and volatilization ratios of plants supplied with SeO 3or SeMeth as compared to those supplied with SeO 4Sulfate level (mM)Se volatilization ratio Se concentration ratio SeO 3/SeO 4SeMeth/SeO 4SeO 3/SeO 4SeMeth/SeO 4Shoot 0.250.4242.90.040.201.00 2.1836.70.060.2410.0 3.26106.20.50 5.10Root 0.250.6434.90.78 3.921.00 1.30239.4 1.8811.810.02.65332.75.3829.5ratio increased from 42.9to 106.2in shoots and from 34.9to 332.7in roots along with the increase in external sulfate level (Table 2).Analysis by XAS of Se in reference compounds and plant tissues.The Se K-edge spectra of reference compounds of SeO 4,SeO 3,Se 0,and SeMeth are shown in Fig.5A.It is apparent by the shift in energy towards more-positive values that the reference compounds'K-edges are sensitive indicators of valence.There was a positive shift in the K-edge along the X-ray energy axis with the formal oxidation state of Se.Similar results have been reported earlier for a wider range of Se reference compounds by Pickering et al.(1995).For this study,the selection of Se reference compounds corresponded to Se treatments.The theory of the K-absorption edge transitions has been clearly discussed and modeled (Kutzler et al.1980).The results for the XAS K-edge analysis for shoot and root tissues of broccoli plants treated with 20l M Se as SeO 4,SeO 3,or SeMeth are shown in parisons of the XAS spectra of the leaf samples (Fig.5B)with those obtained for reference materials (Fig.5A)indicate that Se in the leaves of plants suppliedwith SeO 3or SeMeth is consistent with SeMeth.However,the XAS spectrum of leaves of SeO 4-supplied plants shows that most of the Se remains as SeO 4with a smaller fraction being converted into an organoselenium species most like the SeMeth reference compound.Similar results were observed for root samples treated with SeO 4,SeO 3or SeMeth,except that the fraction of the SeO 4-Se which was converted into organoselenium in SeO 4-supplied plants was much higher in roots than that in leaves (Fig.5C).DiscussionIn¯uence of the chemical species of supplied Se.Varia-tions in the chemical form of the supplied Se greatly in¯uenced the ability of plants to accumulate,translo-cate,and volatilize Se from roots and shoots.In general,plant leaves accumulated Se to the greatest levels when Se was supplied as SeO 4,followed by SeMeth and then SeO 3,while the highest concentrations in roots were attained when SeMeth was supplied,followed by SeO 3,and then SeO 4(Fig.1).Selenium translocation from roots to shoots is best described as SeO 4)SeMethFig.3.Rates of Se volatilization by shoots and roots of Indian mustard,broccoli,rice,and sugarbeet plants supplied with 20l M Se as selenate (SeO 4),selenite (SeO 3),or L -selenomethionine (Se-Met ).Error bars in-dicate SD>SeO3in all plant species studied(Table1).Rates of plant Se volatilization closely corresponded to Se accumulation in roots,except that the di erences in the rates of Se volatilization were greater than the di erences in Se concentrations in roots.This is especially true for plants supplied with SeMeth as compared to those supplied with SeO4or SeO3.Seleni-um volatilization by plant roots was20±73times faster from SeMeth than from SeO3,and was1.2±1.9times faster from SeO3than from SeO4(Fig.3).Selenium concentrations in shoots did not correlate with the rate of plant Se volatilization.This suggests that the rate of Se volatilization depends mainly on the uptake and accumulation of Se by roots.It has already been established that roots are the main sites of Se volatil-ization in plants(Zayed and Terry1994).The question of whether rhizosphere microorganisms are involved in this process or not remains unanswered.The greater di erences we observed in the rates of plant Se volatilization as compared to the di erences in Se concentrations as in¯uenced by the chemical species of Se can be attributed to the ability of the plant to metabolize di erent forms of Se.Chemical speciation of selenium in plant tissues using the XAS technique showed that when plants were supplied with Se as SeO4,most of the Se remained unchanged in the leaf and root tissues with a small portion being converted to a SeMeth-like species;this portion being greater in roots than in leaves.However,plants supplied with SeO3or SeMeth contained only SeMeth-like Se species in both roots and leaves,with no SeO4,SeO3,or Se0observed. These data provide evidence for the rapid reduction of SeO3by plant roots and suggest that the reduction of SeO4to SeO3is a rate-limiting step in the production of volatile Se compounds by plants.This is because SeO3-supplied plants volatilized Se slightly faster than those supplied with SeO4,despite the fact that total Se uptake and accumulation was much greater in SeO4-supplied plants(4-to5-fold higher)than in SeO3-supplied plants.The much greater rates of Se volatiliza-tion from SeMeth-supplied plants,compared to SeO4 and SeO3-supplied plants,can be attributed to:(i)the overall rate of SeMeth uptake being similar to(broccoli and Indian mustard)or slightly higher than(sugarbeet and rice)that of SeO4,and much faster than that of SeO3(Table1);and(ii)all the Se in roots and shoots of SeMeth-supplied plants remaining as SeMeth-like spe-cies(volatilization from SeMeth is more favorable energetically than from inorganic Se).The di erences in plant Se uptake and movement due to the variation in the form of Se supplied may be attributed to the fact that plants take up and translocate SeO4,SeO3,and SeMeth by dissimilar mechanisms. Previous research has shown that while the uptake of SeO4and SeMeth was driven metabolically,uptake of SeO3had a major passive component(Ulrich and Shrift 1968;Asher et al.1977;Abrams et al.1990).Arvy(1993) demonstrated that within3h,50%of the SeO4taken up by bean plant roots moved to shoots,while in the case of SeO3most of the Se remained in the root and only a small fraction was found in the shoot.The addition of hydroxylamine,a respiratory inhibitor,to the nutrient solution inhibited SeO4uptake by80%while SeO3 uptake was inhibited only by20%.Similarly,Abrams et al.(1990)showed that SeMeth uptake by wheat seedlings was inhibited by metabolic inhibitors and by anaerobic conditions,suggesting an active uptake of SeMeth bywheat.Fig.4.In¯uence of increasing sulfate sup-ply on Se volatilization in shoots(top)and roots(bottom)of broccoli plants supplied with20l M Se supplied as sodium selenate (SeO4),sodium selenite(SeO3),orL-selenomethionine(Se-Met).Error bars indicate SDEarlier research also showed that Se volatilization by plants might be dependent on the chemical form of Se supplied.Lewis et al.(1974)revealed that 10±16times more Se was released from excised cabbage leaves taken from plants supplied with SeO 3than those taken from plants supplied with SeO 4.In another study,Asher et al.(1967)found that,during oven-drying,roots of SeO 3-grown plants released 11times more volatile Se than did roots of SeO 4-grown plants.Most recently,Zhang and Moore (1997),while investigating Se volatilization in wetland microcosms,found that the concentration ofdissolved organic Se is more important than dissolved inorganic Se in a ecting Se volatilization from wetland plants and sediments.Selenium volatilization by soil microorganisms has also been shown to be an order of magnitude higher from organic Se than from SeO 4,SeO 3,or elemental Se (Doran and Alexander 1977;Reamer and Zoller 1980;Frankenberger and Karlson 1994).Thus,it appears that the biological volatilization of Se by plants and microbes proceeds more rapidly if Se is supplied in more-reduced forms (e.g.SeO 3,or SeMeth as compared to SeO 4).In plants,when Se is supplied as SeO 4,most of the Se is transported to the shoot unchanged with very little Se remaining in roots.The detection of more SeMeth-like species in roots of SeO 4-supplied plants than in shoots partly explains why roots volatilize faster than shoots,despite the fact that roots contain much less total Se.The overall rate of plant Se volatilization is substantially lower from SeO 4than from SeO 3or SeMeth because of the diminished accumulation in roots and lower rate of metabolic Se conversion.On the other hand,when Se is supplied as SeMeth or SeO 4,a large volume of the absorbed Se remains in roots mainly as Se-Meth,leading to greatly enhanced rates of Se volatilizationIn¯uence of sulfate supply.The results of this study showed that increasing sulfate supply had a stronger inhibitory e ect on the uptake and volatilization of SeO 4compared to those of SeO 3or SeMeth (Figs.2,4;Table 2).Previous research has shown that SeO 4uptake and volatilization by plants are strongly inhibited by the presence of high concentrations of sulfate in the growth medium (Pratley and McFarlane 1974;Mikkelsen et al.1988;Mikkelsen and Wan 1990;Zayed and Terry 1992,1994;Barak and Goldman 1997).Antagonistic sulfate/selenate interactions have generally been reported in single cells,excised roots,and whole plants (Lauchli 1993).Several researchers reported reductions in SeO 4concentration in plant tissues ranging from 80%to 100%when sulfate level was increased in the growth medium (Leggett and Epstein 1956;Ulrich and Shrift 1968;Ferrari and Renosto 1972;Barak and Goldman 1997).Previous studies also showed that sulfate has a much greater e ect on plant uptake of SeO 4than SeO 3(Pratley and McFarlane 1974;Gissel-Nielsen 1973).However,other than the results of this study,there are no data available describing the e ects of increasing sulfate levels on SeMeth uptake and volatilizationbyFig.5A±parison of Se K-edge XANES of:A Se 0,SeO 3,SeO 4,and Se-Meth reference solutions (note the shift in energy towards more positive energy as the valence state of Se increases);B broccoli leaf tissues taken from plants previously supplied with 20l M Se as selenate,selenite,or selenomethionine;and C broccoli root tissues taken from plants previously supplied with 20l M Se as selenate,selenite,or selenomethionine (see Materials and methods for details)Table 3.In¯uence of sulfate supply level on shoot/root con-centration ratio of broccoli plants treated with 20l M Se supplied SeO 4,SeO 3or SeMeth at di erent sulfate levels Sulfate level (mM)Shoot/root concentration ratio SeO 4SeO 3SeMeth 0.25 2.640.140.131.00 5.920.190.1210.01.920.180.33plants.The ability of plants to acquire Se in sulfate-rich soils was related to the presence of organic Se forms in these soils(Abrams et al.1990).It is established that plants can actively take up SeMeth from solution (Abrams et al.1990).The question is:Why does sulfate inhibit SeO4uptake more than SeO3and SeMeth? Inhibitory e ects of sulfate on plant Se uptake,and therefore volatilization,are expected to be greater with respect to SeO4as compared to SeO3or SeMeth due to the competition between SeO4and sulfate for the uptake sites in the root cell membranes.Plants take up SeO4by the same carrier in root cell membranes as sulfate (Leggett and Epstein1956;Ferrari and Renosto1972). Selenite and SeMeth,on the other hand,are taken up by other mechanisms.Plant uptake of SeO3is mostly passive(Arvy1993),whereas,the uptake of SeMeth is thought to be mediated by uptake-permeases speci®c for S-containing amino acids.This conclusion comes from the results obtained by Sandholm et al.(1973),who showed that uptake of L-methionine and L-selenome-thionine by the alga Scenedesmus dimorphys was com-petitive,indicating that the two amino acids may be taken up by the same mechanism.It seems likely therefore that sulfur compounds will competitively inhibit the uptake of their Se analogues(i.e.sulfate inhibits SeO4and methionine inhibits SeMeth).How-ever,experiments conducted by Asher et al.(1977) indicate that neither sulfate nor sul®te caused signi®cant reductions in SeO3uptake by tomato root systems.They also reported that the presence of excess sul®te,and not sulfate,enhanced the transport of SeO3in tomato plants.In our study,increasing sulfate levels did not show any consistent e ect on long-distance transport of Se when supplied as SeO4,SeO3or SeMeth,as judged by the shoot/root concentration ratio(Table3).Rates of Se volatilization by SeO4-supplied plants were shown here to be greater than those of SeO3-supplied plants only at the lowest sulfate level (0.25mM).At all other sulfate levels(1±10mM),plant Se volatilization from SeO3exceeded that of SeO4.The abnormally higher rates of Se volatilization from SeO4 at0.25mM sulfate supply,as compared to SeO3,can be explained by the substantially increased Se accumulation in roots and shoots of broccoli at that level of sulfate due to the lesser competition between sulfate and SeO4.No such increase in uptake and accumulation was observed for SeO3at low sulfate levels,leading to larger di er-ences in tissue Se concentrations between the two Se anions.Rates of Se volatilization from SeMeth were faster than those of SeO4at all sulfate levels,but the di erences were greater at high sulfate supply than at low supply.This may be a re¯ection of the di erences in: (i)Se accumulation in roots;and/or(ii)energy require-ments for the formation of volatile Se.This study was supported by grants from the University of California Salinity/Drainage Task Force and the Electric Power Research Institute.The authors thank Stanford Synchrotron Radiation Laboratory for granted beam time(proposal2413MP) and on-line support.ReferencesAbrams MM,Shennan C,Zasoski J,Burau RG(1990)Selenome-thionine uptake by wheat seedlings.Agron J82:1127±1130 Arvy MP(1993)Selenate and selenite uptake and translocation in bean plants(Phaseolus vulgaris).J Exp Bot44:1083±1087 Asher CJ,Evans CS,Johnson CM(1967)Collection and partial characterization of volatile selenium compounds from Medic-ago sativa L.Aust J Biol Sci20:737±748Asher CJ,Butler GW,Peterson PJ(1977)Selenium transport in root systems of tomato.J Exp Bot23:279±291Barak P,Goldman I(1997)Antagonistic relationship between selenate and sulfate uptake in onion(Allium cepa):implications 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