Physicochemical properties, biological impacts and degradation routes of chloroethenes, PBDEs and HBCD.
\r\n\trescue missions especially in difficult to access areas. Precise control of aerial robotic systems with multiple degrees of freedom arms is a challenging task due to the fact that the translational and rotational dynamics of the aerial vehicle are strongly coupled with the dynamics of the manipulator. In order to accomplish complex missions in presence of uncertainties in the environment, to achieve better maneuverability and precise 3D position and attitude control, nonlinear control techniques have been found effective. Swarm robotics (multiple robot working together) is another exciting application of the aerial robotics. This book intends to provide a wide range of readers in applied mathematics and various engineering disciplines an excellent survey of recent studies of aerial robotic systems.
",isbn:"978-1-78984-567-9",printIsbn:"978-1-78984-566-2",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"53805f091c3107536edd2579c9987649",bookSignature:"Dr. Mahmut Reyhanoglu and Dr. Geert De Cubber",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7792.jpg",keywords:"Nonlinear Dynamics, Lagrangian Formulation, Visual Tracking, Way Point, Lyapunov Technique, Backstepping, Surveilance, Emergency Response, Crop Monitoring, Sliding Mode Observer, Nonlinear Filter, Coordinated Control, Leader-Follower",numberOfDownloads:1051,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"December 11th 2018",dateEndSecondStepPublish:"January 15th 2019",dateEndThirdStepPublish:"March 16th 2019",dateEndFourthStepPublish:"May 21st 2019",dateEndFifthStepPublish:"July 20th 2019",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,editors:[{id:"15068",title:"Dr.",name:"Mahmut",middleName:null,surname:"Reyhanoglu",slug:"mahmut-reyhanoglu",fullName:"Mahmut Reyhanoglu",profilePictureURL:"https://mts.intechopen.com/storage/users/15068/images/system/15068.png",biography:"Mahmut Reyhanoglu is presently the Glaxo Wellcome Distinguished Professor of Engineering at the University of North Carolina at Asheville, North Carolina, USA. His extensive research makes use of advanced mathematical techniques and models that arise from fundamental physical principles. His major research interests are in the areas of nonlinear dynamical systems and control theory, with particular emphasis on applications to mechatronics and aerospace systems. He has edited 3 books, and authored/co-authored several book chapters and over 130 peer-reviewed journal/proceedings papers. He served on the IEEE Transactions on Automatic Control Editorial Board and on the IEEE Control Systems Society Conference Editorial Board as an Associate Editor. He also served as International Program Committee member for several conferences and as a member of AIAA Guidance, Navigation, and Control Technical Committee. He is currently serving as an editor of International Journal of Aerospace Engineering.",institutionString:"University of North Carolina",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of North Carolina at Asheville",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:{id:"81429",title:"Dr.",name:"Geert",middleName:null,surname:"De Cubber",slug:"geert-de-cubber",fullName:"Geert De Cubber",profilePictureURL:"https://mts.intechopen.com/storage/users/81429/images/system/81429.png",biography:"Geert De Cubber works at the Unmanned Vehicle Centre of the Belgian Royal Military Academy, where he is leading the research activities of the research group on robotics for high-risk applications. The specialization of this research unit is the development of robots for applications like search and rescue and humanitarian demining. Geert’s main task is to apply computer vision techniques to mobile robots, rendering these robots able to perceive, analyze, and – to some degree – understand their environment.\nGeert was the coordinator of the EU-FP7-ICARUS projectnwhich was a large-scale EU-project dealing with the development of unmanned tools (aerial, ground and marine robots) which can assist search and rescue workers to save human survivors after a major crisis (earthquake, tsunami, typhoon, shipwreck, etc.).",institutionString:"Royal Military Academy",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"242",title:"Aerial Robotics",slug:"aerial-robotics"}],chapters:[{id:"67435",title:"A System for Continuous Underground Site Mapping and Exploration",slug:"a-system-for-continuous-underground-site-mapping-and-exploration",totalDownloads:138,totalCrossrefCites:0,authors:[null]},{id:"67297",title:"Decentralised Scalable Search for a Hazardous Source in Turbulent Conditions",slug:"decentralised-scalable-search-for-a-hazardous-source-in-turbulent-conditions",totalDownloads:140,totalCrossrefCites:0,authors:[null]},{id:"67003",title:"Vision-Based Autonomous Control Schemes for Quadrotor Unmanned Aerial Vehicle",slug:"vision-based-autonomous-control-schemes-for-quadrotor-unmanned-aerial-vehicle",totalDownloads:166,totalCrossrefCites:0,authors:[null]},{id:"67705",title:"Advanced UAVs Nonlinear Control Systems and Applications",slug:"advanced-uavs-nonlinear-control-systems-and-applications",totalDownloads:325,totalCrossrefCites:0,authors:[null]},{id:"68391",title:"Robotic Search and Rescue through In-Pipe Movement",slug:"robotic-search-and-rescue-through-in-pipe-movement",totalDownloads:284,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"194666",firstName:"Nina",lastName:"Kalinic Babic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/194666/images/4750_n.jpg",email:"nina@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"44283",title:"Microbial Degradation of Some Halogenated Compounds: Biochemical and Molecular Features",doi:"10.5772/56306",slug:"microbial-degradation-of-some-halogenated-compounds-biochemical-and-molecular-features",body:'Due to the advance of organic and synthetic chemistry and many applications of man-made organic compounds, lots of xenobiotic chemicals are produced and benefit our life. However, some of them are persistent in the environment and their toxicities are accumulated through the food webs. Due to the potential hazard for human and the ecosystem, the regulations on the usage of these persistent organic pollutants (POPs) and the development of safe decomposition methods are now in great request.
Tetrachloroethene (PCE) and trichloroethene (TCE) (Table 1) have been widely used as dry cleaning solvents and degreasing agents. Due to poor disposal practices and accidental release, they are within the most-abundant groundwater contaminants. Exposure to PCE is injurious to epidermis, kidney and nervous system [1]. It has been classified as a probably carcinogen [2]. Exposure to TCE leads to acute effects on liver, kidney, central nervous, and endocrine systems. It is also associated with several types of cancers based upon epidemiological research [3]. PCE and TCE are regulated in U.S.A. to a maximum contaminant level of 5 ppb. The use of PCE and TCE in the food and pharmaceutical industries has been banned across much of the world since the 1970s. However, these chemicals are still used as a degreasing agent for other demand. Besides, the mono-chlorinated ethene, vinyl chloride (VC), is known as carcinogen that causes liver cancer [4] and the cis-1,2-dichloroethene (cDCE) is harmful to nervous system, liver and blood cells [5].
Polybrominated diphenyl ethers (PBDEs), composed by two phenol rings and linked by one oxygen atom (Table 1), allow maximum ten bromide atom incorporated on the phenol rings to form 209 possible congeners. They have been widely used as flame retardants in many products over more than three decades. Their usage has protected both human lives and their properties from fire damage. PBDEs disrupt the balance of thyroid hormone, lead to reproductive toxicity, hepatic toxicity, immunotoxicity and developmental neurotoxicity in mammals [6, 7]. The toxicity of PBDEs and their metabolites are due to elevated free radicals, DNA damages, cell cycle blockage and apoptosis rate [8, 9]. Among the congeners of PBDEs, the usage of penta-BDEs and octa-BDEs has been banned in the European Union and several states of the USA; tetra-BDE to hepta-BDE have also been classified as POPs and the production of decabromodiphenyl ether (DBDE) will cease in 2013 in USA. However, the concentration of PBDEs in environment remains exponentially increasing because of the consequence of long-term usage [10]. Due to their ubiquitous distribution in the environment, potential toxicity, tendency for bioaccumulation, and the increased accumulation amount in the environment, the fates of PBDEs in the nature is serious concern for public health.
Hexabromocyclododecane (HBCD), a brominated aliphatic cyclic hydrocarbon (Table 1), is another widely used brominated flame retardant (BFR). It has animal thyroidal and developmental toxicity. The toxicity is due to altering the expression and function of metabolic enzymes, increasing hormone turn over and apoptosis [11, 12]. HBCD has been detected widely in biota and abiotic samples [11]. Due to its persistent, bioaccumulative, and toxic properties, HBCD has been proposed as a Substance of Very High Concern under the REACH regulations [13] and included on the USEPA\'s lists of Chemicals of Concern [14]; It is also under screening-level risk assessments to determine if it meets criteria of compounds in the Stockholm Convention and in the UN-ECE Protocol on POPs [15].
Most traditional remediation methods are not suitable for degrading chloroethenes, PBDEs and HBCD. For example, dehalogenation processes under oxidative, alkaline, or irradiation conditions are high cost of energy and treatment reagents [16, 17]. Pyrolysis is only limited for specific contaminated media with high heat conductance [18]. The generation of hazardous by-products is also a problem [19] It has been reported that PBDEs and HBCD could be photo-degraded [20]; however, pollutants accumulated in the soils, sediments, water bodies are not easy approach to light. Recently, the permeable reactive barrier made by zerovalent iron offers a new direction for halogenated compounds remediation [21]. Electrons offered from iron can reduce the halogenated compounds through reductive dehalogenation.
Zerovalent iron is cheaper than above processing methods, and the shortage of low efficiency is compensated by newly developed nanotechnology. Nanoscale zerovalent metals can degrade chloroethenes, PBDEs, and other contaminants with a fast kinetics and high efficiency [22-26]. There are still some limitations by using nano-metals, such as: toxic by-products generation due to incomplete dehalogenation [23], potential hazardous effect from nanoparticles [27], and large requirement for metals. Therefore, it comes to be one of the recent trends in developing nanomaterials with high efficiency and low environmental impact, and combining with other treatment technologies.
Microbiological approaches produce less intervention to the environment and are less expensive than physical or chemical methods. Biodegradation of chloroethenes has been extensively investigated and reviewed [28-30]. Bioremediation for PBDEs and HBCD are just at the beginning. The main objective of this review is to summarize current knowledge of microbial degradation of chloroethene, PBDEs and HBCD, especially from the biochemical and molecular point of view. We also attempt to compare the advantages and drawbacks of the combined approaches which may apply to field remediation.
Biodegradation of chloroethenes, PBDEs and HBCD occurs in various environmental or living samples [31, 32]. In the environment, microorganisms play major roles in the degradation reactions; while intrinsic detoxicification systems in plants and animals bodies metabolize these compounds [31, 33]. In this article, the diverse and complex microbial degradation machineries are presented and compared.
Biotransformation of chloroethenes, PBDEs and HBCD in aerobic or anaerobic environments has been demonstrated (Fig. 1). In aerobic environment, chloroethenes and PBDEs are metabolized with the generation of energy or degraded cometabolically without energy-yield. In anaerobic condition, they are reduced through the energy yielded from the oxidation of electron donors, i.e. reductive dehalogenation or dehalorespiration. Biotransformation of HBCD might mediate through hydrolytic dehalogenation, which may occur either in aerobic or anaerobic conditions. Detail for the each type of reaction and the degraders will be described in the following sections.
Under aerobic conditions, chloroethenes and PBDEs can be oxidized both cometabolically and metabolically (Fig. 1, left part). Metabolic degradation indicates the use of the above compounds as growth substrate. Chloroethenes and PBDEs more easily undergo aerobic transformation with less numbers of halogen substituent.
Metabolic degradation of cDCE and VC as sole carbon and energy source has been reported by many bacteria, such as the Pseudomonas sp. and Bacillus sp. [34, 35]. Using cDCE as auxiliary substrate for growth is much less [35] and not shown in TCE and PCE. After oxidative transformation, the auxiliary substrate may be mineralized or the carbon atoms may be incorporated into biomass. Microbial growth can be confirmed by monitoring the stable isotope fractionation and is suitable for field assessment [36].
On the contrary, cometabolic degradation of chloroethenes occurs fortuitously during the degradation of growth dependent substrates (auxiliary substrates), such as methane, ammonia, or aromatic hydrocarbons. Even cDCE can be cometabolized when VC is metabolic degraded [34]. Cometabolic degradation of TCE, DCE and VC is common [37, 38]. So far Pseudomonas stutzeri OX1 is the only one that could aerobically cometabolize PCE [39]. Therefore, without primary substrate supplement, intrinsic bioremediation with air or nutrients injection alone could not enhance the aerobic cometabolic mechanism and would not cause the microbial degradation of PCE and TCE contaminated sites.
PBDEs could be degraded into phenol or catechols by aerobic microbial through hydroxylation or bond cleavage [33] (Table 2). Sphingomonas sp. SS3 and SS33 can transform mono- or di-halogenated DEs for growth [40, 41]. In addition, Sphingomonas sp. PH-07 could break down several lower-bronimated BDE congeners (up to tri-BDEs) [42]. Other PBDEs degradation bacteria are reported [43]: Rhodococcus jostii RHA1 and Burkholderia xenovorans LB400 transform several lower-brominated BDE congeners (up to penta- and hexa-BDEs); Rhodococcus sp. RR1 transforms di- and mono-BDEs and the Pseudonocardia dioxanivorans CB1190 only degrades mono-BDEs. The transformation by-products include phenol, catechol, halophenol and halocatehol, indicating nonspecific attractions. These degraders might transform PBDEs through cometabolic reactions because auxiliary substrates such as diphenyl ether are supplemented. The Lysinibacillus fusiformis strain DB-1, cometabolically debrominate DBDE with the metabolism of lactate, pyruvate and acetate, is isolated [44].
So far, there is only one degrader been reported can transform HBCD: Pseudomonas sp. HB01 [45]. Since bromide atom was not detected after degradation reaction, such transformation might not through haloelimination. In general, cometabolism requires supplement of auxiliary substrates and there is no energy yielded. Therefore, microorganisms do not favor proceed this kind of reaction. Besides, the dehalogenation reaction is usually incomplete, resulting accumulation of toxic intermediates. The contribution on the bioremediation from cometabolism is limited [30].
Reductive dehalogenation is an anaerobic respiration process. Electron donors are oxidized and the halogenated compounds are reduced through accepting the electrons. The free energy generated from this reaction supports the growth of microbial degraders. Hydrogen atom replaces the halogen atoms one after another resulting in the dehalogenation sequence from higher-numbered compounds to lower-numbered ones. Contrary to aerobic degradation, the potential for reductive dehalogenation increases with the number of halogenated substituent [29].
Hydrogen gas is generated primary by fermentative and acetogenic bacteria (Fig.1, right part). The dehalorespiration bacteria compete with hydrogenotrophs, such as sulphate-reducers, nitrate-reducers, methanogens, acetogens, and other reducers [46, 47]. Except hydrogen, other electron donors also can be used for reductive dehalogenation, ex. Sulfurospirillum multivorans can also use pyruvate and formate [48].
Several mixed cultures and pure strains are known to reductively transform chloroethenes. Mixed culture could cooperate and transform PCE to ethene. The pure strains belong to different genus, such as Bacillus, Dehalobacter, Dehalococcoides, Desulfitobacterium, Geobacter, and Sulfurospirillum (Table 2). Most of them only dechlorinate PCE and TCE to cDCE. Only Dehalococcoides ethenogenes strain 195 can reductively dechlorinate PCE to ethene. The accumulated hazardous DCE and VC is a major obstacle in bioremediation of chloroethene contaminated sites. Dehalococcoides sp. strain BAV1, dechlorinates DCE and VC and cometabolizes PCE and TCE; the accumulated toxic compounds can be transformed into benign ethene [49].
Reductive debromination of PBDEs has been reported through pure strains (Table 2) or mixed cultures. Most of the debromination processes require TCE to be co-substrate. 20 mixed microcosms can degrade octa-BDE mixture to hexa- to mono-BDEs within 2 months [50]. Sulfurospirillum multivorans could debrominate DBDE into hepta- and octa-BDEs after 2 months of incubation. D. ethenogenes strain 195 could debrominate the octa-BDE mixtures into hepta- to di-BDEs after 6 months of incubation [51]. Dehalococcoides sp. Strain DG could degrade octa-BDE mixture into terta- and penta-BDEs or transform penta-BDE mixture into terta-BDE [52]. Several dechlorinating bacteria, Desulfitobacterium hafniense PCP-1, Dehalobacter restrictus PER-K23, Desulfitobacterium chlororespirans Co23 and Desulfitobacterium dehalogenans JW/IU-DCI debrominate the octa-BDE mixture and the most frequently detected congeners, penta 99 and tetra 47 when PCP, PCE, 3-chloro-4-hydroxybenzoate, or 3-chloro-4-hydroxyphenylacetate are applied as co-substrates [53]. Some mixture cultures do not need halogenated compounds to stimulate PBDEs transformation [50]. Recently, a lactate-dependent bacterium, Acetobacterium sp. strain AG, was isolated and can transform penta-BDE mixtures without other halogenated electron acceptors [52]. We also found that the cometabolism with glucose facilitated the biodegradation of mono-BDE, in terms of kinetics and efficiency in one anaerobic sludge in Taiwan [54].
In a mix microcosm, anaerobic environment necessary for dehalorespiration could be established by other symbiotic microorganisms. In our previous study, the mono-BDE is transformed to diphenyl ether in an aerobic culture from sewage sediment, indicating an anaerobic debromination reaction occurred. The enriched Clostridiales specie shown in the denatured gradient gel electrophoresis (DGGE) may responsible for such reaction [55].
The metabolic pathway of VC is much clearer than that of cDCE. Alkene monooxygenase (AkMO) involves in the initial epoxidation step. The encoded genes (etnABCD) and the structures have been identified. Downstream events of the transformation are mediated through coenzyme M transferase (encoded by etnE gene), alcohol/aldehyde dehydrogenase, CoA transferase and CoM reductase/carboxylase. The final product, acyl-CoA, is then metabolized through TCA cycle [56]. Proteomic and transcriptomic analyses have confirmed the roles of above enzymes in aerobic VC transformation process.
Aerobic cometabolic degradation of chloroethenes is supposed through several kinds of oxygenases: toluene monooxygenase, toluene dioxygenase, phenol monooxygenase and methane monooxygenase [57]. P. stutzeri OX1 depletes PCE and releases chloride irons when toluene is applied as an auxiliary substrate [39]. PCE, DCE, and VC could be transformed by the purified toluene-o-xylene monooxygenase (ToMO). ToMO is a four-component enzyme which consists a catalytic oxygen-bridged dinuclear center encoded by touABE, a NADH ferredoxin oxidoreductase (encoded by touF), a mediating protein (encoded by touD), and a Rieske-type ferredoxin (encoded by touC). The touA~F genes cloned into E. Coli could make it to be PCE-degradable.
Different dioxygenases are supposed to involve in aerobic degradation of lower numbered PBDEs. 1,2-dioxygenase is involving in the initial dihydroxylation step when mono-halogenated DEs to be degraded [40]. Downstream degradation processes are supposed through phenol hydroxylases and catechol 1,2-dioxygenase. The transformation by-products range from phenol, catechol, halophenol and halocatechol, indicating nonspecific attack reactions [40, 41]. 2,3-dioxygenase is responsible to dihydroxylate lower numbered PBDEs and their similar chemicals such as DE in the close species Sphingomonas sp. PH-07 [42]. The range of PBDEs transformed by R. jostii RHA1 depends on the types of growth substrate. The enzymes responsible for degradation are inducible [43]. The expression of biphenyl dioxygenase (BPDO) and ethylbenzene dioxygenase (EBDO) are upregulated during PBDEs degradation. Ectopically expression of these enzymes in closed bacteria that bears no PBDEs degradation activity could transform PBDEs. EBDO depleted mono- through penta-BDEs and BPDO only depleted mono-, di- and one tetra-BDEs. [58].
The structures of HBCD and hexachlorocyclohexanes (HCHs) are quiet similar. Heeb et al. purified the HCH-converting haloalkane dehalogenase LinB, from Sphigobium indicum B90A and applied the enzyme for HBCD degradation. LinB transforms HBCD into pentabromocyclododecanols (PBCDOHs) and further tetrabromocyclododecadiols (TBCDDOHs) [59]. Whether LinB or other haloalkane dehalogenase are the de novo HBCD degradation enzyme is unknown. What enzyme responsible for HBCD degradation in Pseudomonas sp. HB01 is also waited to be uncovered.
Reductive dechlorination reactions are catalyzed by reductive dehalogenases (RDases) The purified PCE RDase, PceA, has proved to transform PCE and TCE to cDCE [60]. The function of TCE RDase, TceA, in transforming TCE to ethene has also been identified [61]. VcrA and BvcA catalyze the transformation of DCE to ethene [62, 63]. In addition to chloroethenes, RDases also could reduce other chlorinated compounds.
RDases which could debrominate PBDEs have not yet been identified. However, some PBDEs degradation bacteria also could transform chloroethene (Table 2), such as Dehalococcoides sp., Desulfitobacterium sp., and Sulfurospirillum sp. Whether these microorganisms use chloroethene RDases to transform PBDEs is unknown. It is also possible that enzymes with different degradation activities or substrate specificity within single degrader may cooperatively transform different PBDEs congeners.
Most RDases presented similar features and conserved motifs [28, 29]. In the N-terminus, RDases possess a putative signal sequence containing the twin-arginine translocation (Tat) motif. Such motif is presented in secretary proteins to be transported across the cytoplasmic membrane through the Tat export system. It is proposed that newly synthesized RDase proteins is folded with cofactors (corrinoid and iron-sulfur clusters) in the cytoplasm with the aids of chaperone proteins. The Tat sequence is then proteolytically cleaved during the maturation process. In the C-terminus, two iron-sulfur cluster binding motifs are presented. The Fe-S clusters cooperate with corrinoid, transfering electrons from upstream donors to chloroethenes and thus catalyze the dehalogenation reaction [28].
The localization of chloroethene RDases is supposed in the membrane, where they could accept electrons from proton producing hydrogenase via menaquinone. The membrane-bound characteristics of RDases has been proved, such as PceA of D. ethenogenes. The localization of constitutively expressed PceA in S. multivorans was initially found in the cytoplasmic fraction [65]. John et al. used freeze-fracture replica immunogold labeling technique and found it would be at the cytoplasmic membrane when cells grown on pyruvate or formate as electron donors [66].
The major catalysis reaction of RDases is directed by subunit A, encoded by reductive dehalogenase homologous A (rdhA) genes. Over 650 rdhA genes have been identified based upon genomic sequence annotation or homologous cloning [67]. However, most of them are not yet been functional characterized. It is common for one dehalorespiration bacterium baring multicopy of rdhA genes in the genome. Besides tceA and pceA genes, there is still 17 RDase genes with unknown function in the genome of D. ethenogenes strain 195 [68]. Whether the roles of these genes are relevant to dehalogenation remains unclear.
Most rdhA genes are organized with genes encoding for accessory proteins. The pce-gene cluster from D. hafniense strain Y51 constitutes pceA followed by pceB, pceC and pceT [69]. PceT is the trigger factor involving in folding newly synthesized polypeptides. It interacts with the Tat motif of PceA, thus solubilizing and stabilizing PceA polypeptide proceeding downstream maturation and transportation processes [62]. PceB protein contains three transmembrane domains and is assumed as a membrane anchor protein of PceA. PceC contains six-transmembrane domains, an FMN binding domain, and a C-terminal polyferredoxin-like domain. It is similar to the membrane-binding transcription regulators [28, 60]. More examples for the organization of different RDase gene clusters are presented in [28]. RdhA and rdhB genes usually locate adjacent and are the basic components of the rdh gene cluster. They would co-express in order to perform dehalorespiration together.
The expression and silence of RDases during dechlorination reaction is dynamic and regulated. It could be monitored through the amount of RNA or protein. Dehalococcoides sp. strain MB bares 7 RDase genes. Only dceA6 is highly expressed when PCE and TCE are transformed into tDCE. Transcription regulation protein binding site related to gene expression is detected in the upstream of dceA6 gene [70]. A shotgun metagenome microarray is created to investigate gene transcription in a mixed culture. rdhA14 and rdhB14, are the only two with higher transcript levels during VC degradation, while another 4 rdhA genes has higher transcript levels in the absence of VC [71]. The absolute quantification of RDase proteins during the dechlorination process is performed by using nano-liquid chromatography-tandem mass spectrometry in two PCE/TCE degradation consortia. Within 5 selected RDases, only the quantities of PceA and TceA are detectable [72]. The regulation on the expression of rdh genes during dechlorination reaction or steady state is not clear. How the physiological environments affect the gene expression is also unclear. Uncover these questions would be helpful for environmental monitoring and remediation.
The complete dehalogenation requires different microorganisms which bear various functions in degradation or growth support. Besides, it competes with methanogens and other reducers for H2. The snapshot of the microbial composition stands for specific ecological condition. The dynamic of composition reveals the effect of various remediation treatments and the interaction between microorganisms. The microbial compositions when co-incubated with zerovalent iron (ZVI) are analyzed by DGGE. The enrichment of iron-reducing bacteria would support the reduction activity of iron for multiple rounds of reactions; the enrichment of nitrate-reducing bacteria also facilitates the cometabolic dehalogenation. These may due to the synergistic effect [54]. Terminal restriction fragment length polymorphism (TRFLP) analysis is also used to analyze the microbial compositions [73]. The resolution limitation of these techniques makes underestimating the complexity of a community. Therefore, new technique is needed to detect specific microbes that are responsible for a key biodegradation process while present in the communities in low numbers. The 16S rRNA genes within a community could be analyzed by recently evolved pyrosequencing or phylogenetic microarray (PhyloChip). PhyloChip composes ten thousands unique 16S rRNA genes. The microbial compositions in TCE contaminated groundwater that is biostimulated or bioaugmented are analyzed. The increase of methanogens at late treatment stage coincident with the increase in methane concentration [74].
There is no close phylogenic relation among diverse dehalorespration degraders. Horizontal gene transfer (HGT) though transposable elements, transmissible plasmids or phage infections is assumed for such convergent evolution. Phylogenetic analyses of the sequences of rdh operon and the adjacent genomic structures support HGT. The pceABC operon in D. hafniense strain TCE1 has been shown to be presented in a circularized transposable element, Tn-Dha1 [75].The single-copy transfer messenger RNA gene (ssrA) essential in bacteria is a common target for mobile element. Integration of mobile element results in the duplication of ssrA gene around transported gene cluster. Many strain-specific rdhA genes collocates within such structures and in a region of high genomic variability between Dehalococcoides strains [76]. According to the metagenomic sequence analysis, one prophage element is located adjacent to tceAB genes in the Dehalococcoides-containing consortium, KB-1. The failure in detecting tceA gene expression in virus and the more closed transposase genes indicating higher possibility for HGT through transposable elements [71]. It seems that dehalorespiration degraders do no acquire RDase genes through single way. This would increase the diversity of degraders and function of RDase, which is advantageous for remediation.
The degradation rate of natural attenuation is slower than chemical or physical treatments. Biostimulation or bioaugement are common strategies for bioremediation of chloroethene [30] Chemical supplements such as potassium permanganate or oxygen injection which can increase oxygen concentration are benefit for microbial dechlorination [77]. Kuo and his collages set biosparging wells for injection substrate and air into TCE contaminated area. Above 95% TCE was removed through cometabolic reactions because the elevated chemical oxygen demand (COD), microbial population, oxidation-reduction potential (ORP) and specific degrading genes after the supplement of substrate [78]. Shortage of auxiliary substrates or accumulation of toxic intermediates also decrease the dehalogenation effect, combined remediation methods may recover the above drawbacks.
Sequential anaerobic/aerobic biodegradation is one of the approaches to accelerate the degradation of recalcitrant halogenated compounds. Anaerobic degraders could target the higher-numbered halogenated compounds. Aerobic degraders only process lower-numbered halogenated compounds. They could transform the by-products produced from anaerobic degradation to antoxic compounds through metabolic or cometabolic reactions. Integration of these two systems makes it possible for complete mineralization.
Chloroethene and PBDEs can be depleted by microscale or nanoscale zerovalent metals [79, 80]. Preliminary dehalogenation of highly halogenated compounds by the reduced metal to generate less halogenated byproducts those are susceptible for microbial degradation. Therefore, the integration of zerovalent metals with biodegradation promotes the dehalogenation efficiency of each type of remediation methods. Reductive debromination of DBDE with nanoscale ZVI (nZVI) results various intermediates ranging from nona-BDEs to tri-BDEs. The known aerobic PBDEs degrader, Sphingomonas sp. strain PH-07, which is able to grow in the presence of nZVI, aerobically mineralizes the low brominated-DEs (tri-BDEs – mono-BDEs) from nZVI treatment [81]. The interactions between metals and microbes are complicated and delicate. H2 generated from the oxidation of metals promotes the growth of some dehalorespiration microbes. Some microorganisms could reduce oxidized metals for multiple runs of reductive reactions or degrade target compounds through cometabolism. Co-incubation with ZVI, microbes in the DBDE-degrading anaerobic sludges hinders the accessibility of MZVI to DBDE and reduced the removal ability in initial stage. However, the synergistic effect in DBDE degradation appears later on. According to the analysis of the microbial community change, co-incubation with MZVI leads to the enrichment of heterotrophic microbial populations bearing nitrate- or iron-reducing activities. The interaction between MZVI and microbes contributed to the synergistic effect [54]. Not only is the growth of microbes affected by metals, but also the expression of functional RDases. Bare nZVI down-regulate the expression of tceA and vcrA genes while coated particles up-regulate their expression [82]. In addition to the reduced metals, combining the electro-fenton process in aerobic degradation is also a newly evolved and potential way in bioremediation. Application of electrolysis also stimulate the microbial reductive dechlorination and oxidative activities [83].
There are advantages by using each type of remediation approach, while there are also limitations and drawbacks. Combining biodegradation with other abiotic/biotic degradation approaches could overcome their weakness and accelerate the degradation efficiency. The recalcitrant halogenated compounds could be completely mineralized. The impact on environment might also be minimized. Integration of different approaches is a new direction for future investigation.
The current knowledge of microbial degradation of chloroethenes, PBDEs and HBCD, has been summarized and reviewed. The biodegradation of these halogenated compounds through aerobic oxidation, aerobic cometabolization, or reductive dehalogenation are introduced. The correspondent enzymes are discussed from the biochemical and molecular point of view. The structure and function of RDases, as well as gene expression regulation and genomic evolution are the major focus. Integration and sequential anaerobic/aerobic biodegradation or (electro)chemical/microbial degradation are suggested for overcoming the disadvantages of single type of treatment. It is possible to completely mineralize these halogenated pollutants by the combination of bio- and abiotic processes and shows promise for site remediation in natural settings and in engineered systems.
Aerobic degradation and anaerobic reductive dehalogenation reactions of chloeoethenes, PBDEs and HBCD. Circle dot indicates above compounds; star indicates reductive dehalogenation driven by the oxidation of electron donors or occurring cometabolically with other dehalorespiration process; gray arrow indicates the hydrolytic dehalogenation of HBCD.
\n\t\t\t | \n\t\t\t\tPCE/TCE\n\t\t\t | \n\t\t\t\n\t\t\t\tPBDEs\n\t\t\t | \n\t\t\t\n\t\t\t\tHBCD\n\t\t\t | \n\t\t
\n\t\t\t | \n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t
Molecular weight | \n\t\t\t165/131 | \n\t\t\t249.1 ~ 959.2 | \n\t\t\t641.7 | \n\t\t
Water solubility (mg l-1) | \n\t\t\t150/1280 | \n\t\t\t4.8 ~0.02 | \n\t\t\t0.003 | \n\t\t
Toxicitya\n\t\t\t | \n\t\t\tEpidermis, liver and kidney damage, immune- and neuro-toxicity, reproductive and endocrine effects, probably cancer | \n\t\t\tDisrupt the balance of thyroid hormone, reproductive, hepatic, and immunotoxicity, developmental neurotoxicity | \n\t\t\tThyroidal and developmental toxicity | \n\t\t
Abiotic degradation | \n\t\t\tChemical oxidation , irradiation, reduced metals (Fe, Fe/Pd) | \n\t\t\tPyrolysis, photolysis, reduced metals (Fe, Fe/Pd), | \n\t\t\tPyrolysis, photolysis | \n\t\t
Biological degradation | \n\t\t\tAerobic/anaerobic | \n\t\t\tAerobic/anaerobic | \n\t\t\tAerobic/anaerobic(?) | \n\t\t
Physicochemical properties, biological impacts and degradation routes of chloroethenes, PBDEs and HBCD.
a: the toxicity of HBCD is based upon investigation in animal model.
\n\t\t\t | \n\t\t\t\tSubstrate\n\t\t\t | \n\t\t\t\n\t\t\t\tEnd-products\n\t\t\t | \n\t\t\t\n\t\t\t\tGenes c\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\treferences\n\t\t\t | \n\t\t
\n\t\t\t\tAerobic\n\t\t\t | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
\n\t\t\t\t\n\t\t\t\t\tBurkholderia xenovorans LB400\n\t\t\t\t\n\t\t\t | \n\t\t\tHexa-BDE to mono-BDE, | \n\t\t\tHydroxylated-BDE | \n\t\t\t\n\t\t\t | [43] | \n\t\t
\n\t\t\t\tLysinibacillus fusiformis strain DB-1\n\t\t\t | \n\t\t\tDBDE | \n\t\t\tND | \n\t\t\t\n\t\t\t | [44] | \n\t\t
\n\t\t\t\tPseudonocardia dioxanivorans CB1190\n\t\t\t | \n\t\t\tMono-BDE | \n\t\t\tND | \n\t\t\t\n\t\t\t | [43] | \n\t\t
\n\t\t\t\tPseudomonas sp. HB01\n\t\t\t | \n\t\t\tHBCD | \n\t\t\tPBCDOHs, TBCDDOHs | \n\t\t\t\n\t\t\t | [45] | \n\t\t
\n\t\t\t\tPseudomonas stutzeri OX1\n\t\t\t | \n\t\t\tPCE | \n\t\t\tCl-, | \n\t\t\tToMO-touABCDEF\n\t\t\t | \n\t\t\t[39] | \n\t\t
\n\t\t\t\tRhodococcus jostii RHA1\n\t\t\t | \n\t\t\tPenta-BDE to mono-BDE | \n\t\t\tHydroxylated-BDE | \n\t\t\tBPDO- bphAa, EBDO- etbAa1, etbAc\n\t\t\t | \n\t\t\t[43, 84] | \n\t\t
\n\t\t\t\tRhodococcus sp. RR1\n\t\t\t | \n\t\t\tDi-, and mono-BDE | \n\t\t\tND | \n\t\t\t\n\t\t\t | [43] | \n\t\t
\n\t\t\t\tSphingomonas sp. PH-07\n\t\t\t | \n\t\t\tTri-, di-, and mono-BDE | \n\t\t\tCatechol, dibromophenol, dihydroxy mono- and dibromo-BDE | \n\t\t\t\n\t\t\t | [42] | \n\t\t
\n\t\t\t\tSphingomonas sp. SS3\n\t\t\t | \n\t\t\tFluoro-, chloro-, and bromo-DE | \n\t\t\tPhenol, catechol, Halophenol and Halocatehol | \n\t\t\t\n\t\t\t | [40] | \n\t\t
\n\t\t\t\tSphingomonas sp. SS33\n\t\t\t | \n\t\t\tDi-, and mono-; fluoro-, chloro-, and bromo-DE | \n\t\t\tPhenol, catechol, di-, and mono-halophenol, di-, and mono-halocatehol | \n\t\t\t\n\t\t\t | [41] | \n\t\t
\n\t\t\t\t\n\t\t\t\t\tAnaerobic\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t\t | \n\t\t |
\n\t\t\t\t\n\t\t\t\t\tAcetobacterium sp. strain AG\n\t\t\t\t\n\t\t\t | \n\t\t\tPenta-BDE mixturea\n\t\t\t | \n\t\t\tTetra-, tri-, and di-BDEs | \n\t\t\t\n\t\t\t | [52] | \n\t\t
\n\t\t\t\tBacillus sp JSK1\n\t\t\t | \n\t\t\tPCE | \n\t\t\t\n\t\t\t\tCis-DCE | \n\t\t\t\n\t\t\t | [85] | \n\t\t
\n\t\t\t\tDehalobacter restrictus\n\t\t\t | \n\t\t\tPCE, TCE | \n\t\t\t\n\t\t\t\tCis-DCE | \n\t\t\t\n\t\t\t\tpceA, \n\t\t\t | \n\t\t\t[67, 86] | \n\t\t
\n\t\t\t\tDehalococcoides sp. strain BAV1\n\t\t\t | \n\t\t\tDCE, VC | \n\t\t\tEthene | \n\t\t\t\n\t\t\t\tbvcA \n\t\t\t | \n\t\t\t[49, 87] | \n\t\t
\n\t\t\t\tDehalococcoides sp. strain DG\n\t\t\t | \n\t\t\tTCE Octa-BDE mixtureb\n\t\t\t\t Penta- BDE mixturea\n\t\t\t | \n\t\t\tEthene Tetra- and penta-BDEs Tetra- BDE | \n\t\t\t\n\t\t\t | [52] | \n\t\t
\n\t\t\t\tDehalococcoides ethenogenes strain 195\n\t\t\t | \n\t\t\tPCE, TCE, cis -DCE, and VC Octa-BDE mixtureb\n\t\t\t | \n\t\t\tEthene Tetra-, penta-, hexa- and Penta-BDEs | \n\t\t\t\n\t\t\t\tpceA, tceA\n\t\t\t | \n\t\t\t[51, 67, 89] | \n\t\t
\n\t\t\t\tDehalococcoides sp. strain MB\n\t\t\t | \n\t\t\tPCE, TCE | \n\t\t\t\n\t\t\t\tTrans-DCE | \n\t\t\t\n\t\t\t | [90] | \n\t\t
\n\t\t\t\tDesulfitobacterium chlororespirans strain Co23\n\t\t\t | \n\t\t\tOcta-BDE | \n\t\t\tND | \n\t\t\t\n\t\t\t | [53] | \n\t\t
\n\t\t\t\tDesulfitobacterium dehalogenans strain JW/IU-DC1\n\t\t\t | \n\t\t\tPCE, TCE, Octa-BDE, | \n\t\t\tND | \n\t\t\t\n\t\t\t | [53, 67, 91] | \n\t\t
\n\t\t\t\tDesulfitobacterium hafniense PCP-1\n\t\t\t | \n\t\t\tOcta-BDE | \n\t\t\tND | \n\t\t\t\n\t\t\t | [53] | \n\t\t
\n\t\t\t\t\n\t\t\t\t\tGeobacter lovleyi strain SZ\n\t\t\t\t\n\t\t\t | \n\t\t\tPCE, TCE | \n\t\t\tcDCE | \n\t\t\t\n\t\t\t | [92] | \n\t\t
\n\t\t\t\tSulfurospirillum multivorans\n\t\t\t | \n\t\t\tPCE, TCE DBDE | \n\t\t\tDCE Octa- and hepta-BDEs | \n\t\t\t\n\t\t\t\tpceA\n\t\t\t | \n\t\t\t[51, 67] | \n\t\t
Selected bacteria which degrade chloroethens, PBDEs and HBCD.
a penta- BDE mixture: hexa-, penta-, and tera-BDEs.
b Octa-BDE mixture: nona-, octa-, hepta-, and hexa-BDEs.
c Genes that only relevant to degradation of chloroethens, PBDEs and HBCD are listed.
ND: data not shown. BPDO: biphenyl dioxygenase. EBDO: ethylbenzene dioxygenase. PBCDOHs: pentabromocyclododecanols. TBCDDOHs: tetrabromocyclododecadiols.
The authors thank National Science Council (NSC), Taiwan, ROC for financial support.
Heat and mass transfer between multi-phases or different materials with interfacial conjugate conditions is frequently encountered in fundamental sciences and numerous engineering applications involving fluid dynamics, thermal transport, materials sciences, and chemical reactions. Examples are cooling of turbine blades, heat exchangers and electronic devices, thermal insulation on heat pipes and chemical reactors, heat conduction in composite materials, and heat and mass transfer between solid particles and their surrounding fluids [1, 2, 3, 4, 5, 6, 7, 8], to name a few. The most well-known conjugate conditions include the continuity of both the temperature (concentration) and the heat (mass) flux at the interface. Other conjugate conditions, such as with temperature (concentration) jumps and/or flux discontinuities [9], and Henry’s law relationship [10], are also noticed at fluid-solid interfaces or interfaces of two solids or fluids of different thermal (mass diffusion) properties.
\nThe non-smoothness or discontinuities/jumps in the physical or transport properties, and consequently in the distribution of the temperature (concentration) field across the interface, pose a great challenge to any numerical method applied to solve the interface problems. Development of accurate and efficient numerical schemes to treat the interface conditions has attracted much attention in the literature, such as the immersed boundary method (IBM) [11, 12], the immersed interface method (IIM) [13, 14], the ghost fluid method (GFM) [15, 16], the sharp interface Cartesian grid method [17, 18], and the matched interface and boundary (MIB) method [19]. Most of these methods are formulated in the finite-difference, finite-volume or finite-element frameworks.
\nWhen applying those traditional numerical methods, a popular approach to implement the conjugate conditions is to employ iterative schemes, in which a Dirichlet interface condition is imposed for one phase or material and a Neumann interface condition for the other. The heat and mass transfer in each phase is separately solved, and the continuity or prescribed jump condition at the interface could be satisfied after multiple iterations. For conjugate transport with complex interface geometry, the iterative schemes would become difficult to implement and they normally necessitate a considerable amount of computational effort.
\nThe lattice Boltzmann method (LBM), which has emerged as an attractive alternative numerical method for modeling fluid flows and heat mass transfer (see [20, 21, 22] and Refs. therein), has been demonstrated to be an effective and efficient numerical approach for conjugate interface conditions in tandem with the convection diffusion equation (CDE) [9, 23]. In this chapter, we present a critical review of the various interface schemes proposed in the literature, with a focus on the comparison of numerical accuracy.
\nThe well-known features of the LBM method include its explicit algorithm, ease in implementation, capability to treat complex geometry, and compatibility with parallel computing [20, 21]. Boundary condition treatment is essential to the integrity of LBM since the kinetic theory-based method deals directly with the microscopic distribution functions (DFs) rather than the macroscopic conservation equations. Earlier LB models treat the collision effects with a single-relaxation-time (SRT) approximation, commonly referred to as the Bhatnagar-Gross-Krook (BGK) model [24, 25, 26]. However, the SRT model is limited such that it can only describe isotropic diffusion [20]. In recent years, models such as the two-relaxation-time (TRT) [27, 28] and multiple-relaxation-time (MRT) [20, 29, 30] LB models have been proposed that can handle anisotropic diffusion. Representative LB models proposed in the literature include the general BGK model by Shi and Guo [31] for the nonlinear CDE, the D3Q7/D2Q5 MRT models by Yoshida and Nagaoka [20] for the general convection anisotropic diffusion equation, and the D1Q3/D2Q9/D3Q19 MRT models by Chai and Zhao [30] for the general nonlinear convection anisotropic diffusion equation, to name a few. The MRT models have improved numerical accuracy and stability compared to the SRT models [20, 28, 30]. The D3Q7/D2Q5 model proposed in [20] is used for this review, as it preserves second order spatial accuracy when recovering the general CDE following an asymptotic analysis. Based on the D3Q7/D2Q5 LB models, Li et al. [21] proposed second-order accurate boundary treatments for both the Dirichlet and Neumann conditions; they have also established a general framework for heat and mass transfer simulations with direct extension to curved boundary situations. In their framework, explicit analytical expressions were developed to relate the macroscopic quantities, such as boundary temperature (concentration) and their fluxes, and interior temperature (concentration) gradients, to the microscopic DFs in the LB model.
\nThe first work that explicitly addressed the fluid-solid interface condition in LBM was conducted by Wang et al. [6]. They proposed a simple “half lattice division” (HLD) treatment in which no special treatment is required and the temperature and flux continuity condition at the interface was automatically satisfied for steady cases. Improvement of this HLD based scheme was conducted by several groups (see a review in Ref. [23]) for unsteady cases. Importantly, with the interface treated as a shared boundary between the adjacent domains, the boundary conditions by Li et al. [21] were applied to interface conditions and particular interface schemes were proposed and verified in [23] for standard conjugate conditions, and in [9] for conjugate heat and mass transfer with interfacial jump conditions. This idea of developing analytical relationships for the DFs to satisfy the conjugate conditions was also extended to handle general interface conditions in [32, 33]. In all the previous schemes in [9, 23, 32, 33], the local geometry was taken into account and the second-order accuracy of the LBM solution can be preserved.
\nThere is another category of interface schemes that has attracted interest in the LBM community. In those schemes, additional source terms [34], alternative LBE formulations [35, 36], or modified equilibrium DFs [37, 38], were proposed to handle the conjugate conditions. The main motivation for those schemes is to avoid the consideration of the interface geometry or topology, which can be a challenge in complex systems such as porous media. As pointed out in [33], however, these schemes usually suffer from degraded numerical accuracy and/or convergence orders. This perspective will be illustrated in detail in this chapter.
\nThe rest of this chapter is organized as follows. In Section 2, the different types of conjugate conditions are presented. The LB model for the general CDE is briefly described in Section 3. In Section 4, the representative interface schemes are summarized. Discussion on the numerical accuracy of the selected interface schemes is provided in Section 5 with representative numerical examples. Conclusions and outlook are given in Section 6.
\nIn order to define the conjugate conditions, we begin by defining two domains 1 and 2, as shown in Figure 1. The conjugate interface conditions, from a heat and mass transfer perspective, can be defined as:
\nIllustration of the local geometry of an interface in the lattice (filled circles: lattice nodes in domain 1, filled squares: interface nodes, and open circles: lattice nodes in domain 2). With permission from [9].
where n represents the normal direction, \n
Some examples of jump conditions can be found in cases such as concentration jumps (Henry’s law [10]) or temperature jumps at the interface [9]. Eqs. (1a)–(1c) reduce to the standard conjugate conditions in [23] with no jumps and zero normal velocity; they can also be extended to yield two general relationships between interfacial scalar values and their fluxes as in [32, 33].
\nThe governing heat and mass transfer equation within each domain can be written as a general convection diffusion equation as:
\nwhere \n
For fluid flow simulations, the D2Q9/D3Q19 LB models are the most popular selections due to their accuracy and robustness [39]. While for the scalar CDE (2), the D2Q5/D3Q7 LB models are most widely used [20, 40]. To recover the CDE to second-order accuracy, the evolution equation follows
\nwhere the microscopic distribution function, gα(x, t) \n
The equilibrium distribution function can be defined as [20, 40]
\nwhere cs is the speed of sound with \n
When using the multiple-relaxation-time (MRT) collision operator, and the collision-streaming process for efficient computations, Eq. (3) is split into two steps:
\nIn the above, M is a matrix to transform the distribution functions \n
One unique feature of the LBM method is that both the Dirichlet-type boundary value and the Neumann-type boundary flux, i.e., temperature/concentration gradient, can be obtained from a simple moment of the distribution functions with appropriate boundary schemes [20, 21]. It eliminates finite-difference type approximation schemes for the flux. This idea can also be applied to construct interface schemes by treating the interface as a shared boundary between the two adjacent domains [23, 32, 33, 9]. We consider the basic situation with zero convective flux (un = 0) and the normal of the interface parallel to the discrete velocity vector, i.e., parallel straight interface; the interface scheme for more general situations such as curved geometry can be similarly constructed as shown in [9, 23, 32, 33]. The conjugate conditions in Eqs. (1b) and (1c) thus reduce to (see Figure 1)
\nwith σ = 1, \n
Depending on whether the local interface geometry is considered, the interface schemes fall into two different categories, as discussed in Sections 4.1 and 4.2.
\nAccording to the second-order interpolation-based boundary schemes developed in [21], the relationships between the distribution functions and the interfacial ϕ values and their fluxes for each domains can be obtained: for the Dirichlet condition treatment,
\nSimilarly, for the Neumann condition treatment,
\nwhere \n
When \n
The coefficients in Eqs. (11a) and (11b) are now determined by the geometry fraction Δ and the property ratio σ. It is worth noting that there is an adjustable parameter in those coefficients since the second-order Dirichlet boundary scheme allows one adjustable parameter, as shown in [21], where three particular Dirichlet schemes were also presented:
\nThe corresponding interface schemes, can thus also be obtained. Those schemes will be numerically verified in Section 5.2 for a test case including interfacial jump conditions.
\nWhen the straight interface is located “halfway” between the lattice nodes (∆ = 0.5), the unknown DFs can be calculated by only knowing two single-node post-collision DFs, i.e., without interpolation:
\nFurthermore, for the most simplified case of ∆ = 0.5 and σ = 1, Eqs. (13a) and (13b) reduce to:
\nFor straight interfaces where Δ ≠ 0.5 and for curved interfaces, the complete interface conditions can be found in [9, 23].
\nIt should be noted that second-order accurate boundary schemes can also be obtained using only the single lattice node next to the boundary, as demonstrated in [32], instead of using interpolations in Eqs. (9) and (10). However, such boundary schemes were constructed with complex coefficients that are related not only to geometry-related Δ, but also to the LB model-related relaxation coefficient. Interested readers are referred to [32] for details and such interface schemes are not discussed in this chapter.
\nThe second-order interpolation based interface scheme developed in [23] has attracted much interest. In the last 5 years, there have been various modified interface schemes proposed with the objective of simplifying the original scheme, as it becomes complex and computationally expensive when applied to curved or irregular interfaces. The applicability of those modified schemes was demonstrated in those publications while their accuracy and convergence order have not been fully investigated. In this section, we present three groups of those modified schemes that do not account for the local interface geometry. Most of those schemes were formulated for conjugate heat transfer problems, and conjugate mass transfer can be similarly handled. Comparison of their numerical accuracy with the original interpolation based scheme will be presented in Section 5.1.
\nIn the first group, additional source terms were introduced to the lattice nodes in the domains next to the interface. For example, the following additional source was given in [34]:
\nIn LBM, the total flux in the second bracket can be conveniently obtained from the moment of the nonequilibrium DFs [20, 21, 34]. In [34], the gradient of the heat capacity-related term in Eq. (15) was computed from a first-order one-sided finite-difference (FD) scheme: \n
It should be noted that the above introduction of the source term and the calculation of the heat capacity-related gradient cause two issues: first, for adjacent domains with distinct \n
Another area of interest is found in enthalpic formulations for the LBE. With the definition of an “enthalpic term” \n
Comparing Eq. (16) with Eq. (2), the last two terms in Eq. (16) need to be included in the source term implementation in LBM simulations. Clearly, those additional terms also have the heat capacity-related gradients and thus the discontinuity effect. In [36], the gradient was approximated from \n
In this group, modified equilibrium DFs were introduced. The heat capacity is typically involved in the modified equilibrium DFs, such as [37, 38]
\nWhen using the MRT D2Q5 model the equilibrium moments are calculated to be
\nThe temperature is solved from \n
In order to verify the applicability of the different interface schemes to simulate conjugate heat and mass transfer and compare their accuracy, we consider two benchmark cases with analytical solutions: (i) 2D convection-diffusion in a channel with two-layered fluids, and (ii) 2D diffusion within a circular domain of two solids with interfacial jump conditions. The computational domains are depicted in Figures 2 and 9, respectively. Those two cases have been widely employed in [9, 23, 32, 33, 34] to verify the various interface schemes.
\nSchematic layout of the lattice on a 2D channel containing two fluids in domain 1 (0 ≤ y ≤ h) and domain 2 (h ≤ y ≤ H). With permission from [23].
For straight interfaces, the following relative L2 norm errors are defined to check the numerical accuracy and convergence orders following [23]:
\nwhere E2 evaluates the overall error in all the interior lattice nodes in the two domains, E2, ϕint and E2, qint evaluate the respective relative errors of the interfacial ϕ value and its flux. For circular interfaces, E2, ϕint and E2, qint are evaluated at the interface nodes along the curved geometry. The computation of the interfacial quantities follows that in [21, 23].
\nThe computational domain is depicted in Figure 2. The two fluids are assumed immiscible and both have the same velocity u = (U, 0). The characteristic Péclet number, Pe, is defined as Pe = UH/D1.
\nWhen considering isotropic diffusion, the governing CDE can be expressed as
\nWe consider only the steady case with sinusoidal boundary conditions on the horizontal walls \n
The interpolation-based interface scheme in Section 4.1 and the modified schemes in Groups 1–3 in Section 4.2 are implemented with the D2Q5 MRT-LB model. For all cases presented, Pe = 20 is used with H = 2 h.
\nFigures 3a and b show the scalar value and flux profiles at selected locations along the x-direction using a diffusivity ratio D21 = D2/D1 = 2 and a thermal conductivity ratio k21 = k2/k1 = 3 (consequently \n
Profiles of the (a) scalar variable, and (b) flux values at selected vertical lines in the channel.
Comparison of (a) interfacial scalar value, and (b) interfacial fluxes.
Figures 5, 6, 7 show the L2 errors defined in Eqs. (19)–(21), respectively. The physical and geometric parameters are D21 = 10, k21 = 100, σ = 10, and Δ = 0.5. Simulation parameters in LBM include τ1 = 0.55, τ2 = 1.0 for the original scheme [23] and Groups 1 and 2 with (τ2−0.5)/(τ1−0.5) = D21, and τ1 = 0.5025, τ2 = 0.75 for Group 3 with (τ2−0.5)/(τ1−0.5) = k21.
\nRelative L2 norm error, E2, for the interior scalar versus the grid resolution, 1/H, for steady convection-diffusion in the channel at Δ = 0.5.
Relative L2 norm error, E2, ϕint, for the interfacial scalar versus the grid resolution, 1/H, for steady convection-diffusion in the channel at Δ = 0.5.
Relative L2 norm error, E2, qint, for the interfacial flux versus the grid resolution, 1/H, for steady convection-diffusion in the channel at Δ = 0.5.
Figure 5 clearly shows that the original scheme in [23] and Group 3 are able to preserve the second-order accuracy in LBM. However, Groups 1 and 2 show only a linear convergence at low resolution, and it reduces further towards zeroth-order convergence at high resolution. Similar observations can be found in the errors for the interfacial scalar values in Figure 6. For the interfacial flux errors shown in Figure 7, Group 2 always exhibits zeroth-order accuracy, while Group 1 exhibits linear convergence in one domain and zeroth-order in the other. As previously mentioned, a discontinuity approximation is present in the development of interface schemes in Groups 1 and 2. This is the direct cause for the degradation of the order of accuracy and the much higher error magnitude in these two groups.
\nTo further demonstrate the necessity of taking into account the local geometry to preserve second-order accuracy, Figure 8 presents the L2 norm errors for the interior field at different Δ values. The parameters used are D21 = 5, k21 = 50, σ = 10, (τ1, τ2) = (0.515, 0.575) for the interpolation-based scheme [23] and Groups 1 and 2, and (τ1, τ2) = (0.515, 1.25) for Group 3. For the interpolation-based scheme with an adjustable variable, cd1 = −1 is used.
\nRelative L2 norm error, E2, for the interior scalar versus the grid resolution, 1/H, for steady convection-diffusion in the channel at different Δ values.
It is clear in Figure 8 that only the interpolation-based scheme considering the local geometry is able to preserve second-order accuracy at different Δ values. The error behavior for Groups 1 and 2 is similar to that in Figure 5, the near identical errors at high resolution for different Δ values confirm the error caused by the same discontinuity; moreover, while Group 3 presents second-order convergence for Δ = 0.5, the convergence order drops to first-order for Δ ≠ 0.5. This is similar to the behavior of the “half-lattice division” scheme discussed in [23]. It is evident that when the local interface geometry is not considered for cases Δ ≠ 0.5, the inherent second-order accuracy in LBM computation can be lost.
\nThis test is applied to demonstrate the applicability and accuracy of the interpolation-based interface scheme to simulate transport in complex geometry and with interfacial jump conditions. Figure 9 schematically presents the lattice layout and computational domain for 2D diffusion within a circular domain. On the outer circle with radius, R2, a Dirichlet boundary condition is applied as ϕ2(r = R2) = cosφ. With the following conjugate conditions at the interface:
\nSchematic layout of the computational domain for circular diffusion. With permission from [9].
and
\nthe analytical solution can be found [9]. For the numerical LBM computation, the parameters are R2/R1 = 2, D2/D1 = 10, \n
Figures 10 and 11a,b provide the respective relative L2 norm errors for the interior temperature and the interfacial temperature and flux with the conjugate schemes implemented.
\nRelative L2 norm error, E2, for the interior temperature versus the grid resolution for 2D diffusion in a circular domain. With permission from [9].
Relative L2 norm errors (a) E2_tint for the interfacial temperature, and (b) E2_qint for the interfacial flux, versus the grid resolution for 2D diffusion in a circular domain. With permission from [9].
The results in Figures 10 and 11 demonstrate the first-order accuracy at high resolution for all cases. It should also be noted that the temperature and flux jump conditions at the interface do not affect the order of convergence. The decrease of the convergence order from second-order in Section 5.1 to first-order in this test is due to the implementation of the Cartesian decomposition method [21] that was used to convert the normal fluxes into those in the discrete velocity directions. It is expected that the modified geometry-ignored interface schemes in Groups 1–3 would result in much higher error magnitude for curved interfaces, and Groups 1 and 2 would yield only zeroth-order convergence for all the three quantities of interest. This will be presented in future publications.
\nThe chapter presents a brief review of the interface schemes within the scope of the lattice Boltzmann method (LBM) for conjugate transport between multiphases or different materials. Compared to the interface schemes developed to satisfy the macroscopic conjugate conditions using traditional CFD methods, the LBM method deals with the microscopic distribution functions (DFs); the physical conjugate conditions can be converted to those for the DFs, and they are satisfied in each time-marching step without iterations. In the last decade, a number of interface schemes have been proposed. The interpolation-based schemes [23, 33, 9] taking into account the local interfacial geometry are able to preserve the second-order accuracy in LBM for straight interfaces; while those “modified” geometry-ignored schemes [34, 35, 36, 37, 38] have at most first-order accuracy in general, and with the introduction of heat capacity-related discontinuity in those schemes (e.g., Groups 1 and 2), the order of accuracy becomes essentially zeroth order.
\nFurthermore, it is verified that when using the interpolation-based schemes, the interfacial jump conditions can be conveniently modeled with no effect on the order of accuracy of the LBM solutions.
\nCurved geometry also has a substantial effect and it reduces the order of accuracy of LBM solutions in modeling conjugate heat and mass transfer problems. In addition, the interpolation-based schemes would demand for a higher computational cost than those modified schemes. The readers are thus recommended to take into account both numerical accuracy and computational cost when selecting effective interface schemes for curved geometries.
\nLL acknowledges the support and start-up fund from the Department of Mechanical Engineering at Mississippi State University.
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",metaTitle:"Prior Publication Policy",metaDescription:"Prior Publication Policy",metaKeywords:null,canonicalURL:"/page/prior-publication-policy",contentRaw:'[{"type":"htmlEditorComponent","content":"A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
\n\nThe significance of Peer Review cannot be overstated when it comes to defining, in our terms, what constitutes a published scientific work. Peer Review is widely considered to be the cornerstone of modern publishing processes and the key value-adding contribution to a scholarly manuscript that a publisher can make.
\n\nOther than the issue of originality, research misconduct is another major issue that all publishers have to address. IntechOpen’s Retraction & Correction Policy and various publication ethics guidelines identify both redundant publication and (self)plagiarism to fall within the definition of research misconduct, thus constituting grounds for rejection or the issue of a Retraction if the work has already been published.
\n\nIn order to facilitate the tracking of a manuscript’s publishing history and its development from its earliest draft to the manuscript submitted, we encourage Authors to disclose any instances of a manuscript’s prior publication, whether it be through a conference presentation, a newspaper article, a working paper publicly available in a repository or a blog post.
\n\nA note to the Academic Editor containing detailed information about a submitted manuscript’s previous public availability is the preferred means of reporting prior publication. This helps us determine if there are any earlier versions of a manuscript that should be disclosed to our readers or if any of those earlier versions should be cited and listed in a manuscript’s references.
\n\nSome basic information about the editorial treatment of different varieties of prior publication is laid out below:
\n\n1. CONFERENCE PAPERS & PRESENTATIONS
\n\nGiven that conference papers and presentations generally pass through some sort of peer or editorial review, we consider them to be published in the accepted scholarly sense, particularly if they are published as a part of conference proceedings.
\n\nAll submitted manuscripts originating from a previously published conference paper must contain at least 50% of new original content to be accepted for review and considered for publication.
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\n\nNewspaper and magazine articles usually do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense. Articles appearing in newspapers and magazines rarely possess the depth and structure characteristic of scholarly articles.
\n\nSubmitted manuscripts stemming from a previous newspaper or magazine article will be accepted for review and considered for publication. However, Authors are strongly advised to report any such publication in an accompanying note to the External Editor.
\n\nAs with the conference papers and presentations, Authors should obtain any necessary permissions from the newspaper or magazine that published the work, and indicate that they have done so in a note to the External Editor.
\n\n3. GREY LITERATURE
\n\nWhite papers, working papers, technical reports and all other forms of papers which fall within the scope of the ‘Luxembourg definition’ of grey literature do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense.
\n\nAlthough such papers are regularly made publicly available via personal websites and institutional repositories, their general purpose is to gather comments and feedback from Authors’ colleagues in order to further improve a manuscript intended for future publication.
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\n\n4. SOCIAL MEDIA, BLOG & MESSAGE BOARD POSTINGS
\n\nWe feel that social media, blogs and message boards are generally used with the same intention as grey literature, to formulate ideas for a manuscript and gather early feedback from like-minded researchers in order to improve a particular piece of work before submitting it for publication. Therefore, we do not consider such internet postings to be publication in the scholarly sense.
\n\nNevertheless, Authors are encouraged to disclose the existence of any internet postings in which they outline and describe their research or posted passages of their manuscripts in a note to the Academic Editor. Please note that we will not strictly enforce this request in the same way that we would instructions we consider to be part of our conditions of acceptance for publication. We understand that it may be difficult to keep track of all one’s internet postings in which the researcher´s current work might be mentioned.
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