Characteristics of different media used for water filtration
\r\n\tThis cell has evolved an effective defense system to counteract the challenges as it is always in an oxygen-rich environment. The evolution of hemoglobin and deformability of erythrocyte membrane adapting to its function in circulation is especially striking. Erythrocyte aging and eryptosis strike a balance - the mixed population of cells and constant recycling every 120 days is a very distinct feature. Its metabolic shunt pathways and metabolites/enzymes alter and adapt with age and changes in the microenvironment.
\r\n\r\n\tErythrocyte and its cytoskeleton responses to various situations such as infections, hypoxia, hypothermia, intrigues researchers and biologists alike. This book aims to throw light on the significance of erythrocyte and its characteristic nature and survival in different physiological situations as it plays a very crucial role.
\r\n\r\n\tThis book hopes to bring different perspectives from various aspects and provide insights into the effective mechanisms evolved by erythrocytes, to counteract the challenges faced in its oxidation environment and the further research approaches.
",isbn:"978-1-80356-732-7",printIsbn:"978-1-80356-731-0",pdfIsbn:"978-1-80356-733-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"1b6073b9ff3f8f63004943bd263cd04e",bookSignature:"Dr. Vani Rajashekaraiah",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11725.jpg",keywords:"Erythrocyte, Hemoglobin, Erythrocyte Aging, Pathways, Metabolites, Deficiencies, Membrane Changes, Band 3, Deformability, Hemolysis, Disease Conditions, Free Radical Initiators",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 24th 2022",dateEndSecondStepPublish:"May 26th 2022",dateEndThirdStepPublish:"July 25th 2022",dateEndFourthStepPublish:"October 13th 2022",dateEndFifthStepPublish:"December 12th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Vani Rajashekaraiah, Associate Professor, JAIN (Deemed-to-be University), Bangalore has 20 years of research experience in Oxidative Stress Physiology and Hematology and 16 years of teaching experience. She has authored numerous journal papers and book chapters and has one published patent. She has received CSIR research fellowship and is a Member of the Society for Free Radical Research, India.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"352876",title:"Dr.",name:"Vani",middleName:null,surname:"Rajashekaraiah",slug:"vani-rajashekaraiah",fullName:"Vani Rajashekaraiah",profilePictureURL:"https://mts.intechopen.com/storage/users/352876/images/system/352876.jpg",biography:"Teaching Experience: 16 years\n•\tAssociate Professor in Biotechnology, School of Sciences, Block I, JAIN (Deemed-to-be University), Bengaluru from May 2018 till date. (Courses: Molecular Genetics, Molecular Biology and Genetic Engineering). \n Research Experience: 20 years in the field of Oxidative Stress Physiology and hematology.\n Current Research focus: Blood Storage (erythrocytes, platelets) and Drug-induced Thrombocytopenia\n Total publications in SCOPUS / Web of Science: 27 and International book chapters: 04.\n Research guidance: 3 PhD students (completed); 3 PhD students guiding currently.\n \t \n Six years of research experience as JRF (CSIR) and SRF (CSIR) in the field of High Altitude Physiology and Biochemistry, specialization in Oxidative Stress Physiology, from August 2002 to 2008. \no\tPursued research under the guidance of Dr. S. Asha Devi, Professor, Dept. of \n Zoology, Bangalore University, Bangalore-560056, towards Ph.D in Zoology.\n Title of the thesis- “Studies on Oxidative Stress in Erythrocytes of Rats Exposed to \n Intermittent Hypobaric Hypoxia”.",institutionString:"Jain University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Jain University",institutionURL:null,country:{name:"India"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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A slow sand filter is simple in design, construction, and operation. It is simply a filter box (usually made of concrete) containing sand media supported by a layer of gravel with appurtenances to deliver and remove water. The first recorded use of slow sand filters for a citywide water supply was in 1804 by John Gibbs in Paisley, Scotland (Barrett et al. 1991). Slow sand filters as their name implies, is accomplished with a relatively slow speed of filtration (typically 0.1 to 0.2 meters per hour) with 1 to 2 meters media depth. Because of the slow filtration rate, the head loss buildup is gradual and usually takes several months to achieve a significant level and form a condensed layer called
Sand, anthracite, and granular activated carbon or their combination was used as media with proper gradation. Some proprietary media as pumice, expanded clay, diatomaceous earth, and ceramic have also been applied. Characteristics of different media are shown in Table 1.
When media becomes clogged and dirty, the best way is to backwash the filter and flush dirt out. Backwash is classified as fluidized backwash and sub-fluidized backwash. Fluidized backwash requires a higher water rate to expand media bed usually by 20-30%, where the minimal fluidization water velocity is directly related to media type, media size, uniformity coefficient, water temperature, and salinity (important factor for sea water filtration) (Logsdon et al. 2002). However, a fluidization test is always recommended for precisely identifying the backwash rate to achieve the desired fluidization. Usually sub-fluidized backwash is applied to coarse media with 15-20 m/h, where media will only move or rotate locally and expand slightly as fluidization of larger media require an extremely high water velocity (Logsdon et al. 2002). Backwash can be water only backwash or air assisted backwash, the later of which has gained popularity because of water conservation and effectiveness of media cleaning. The air scouring (usually at 70-90 m/h) can be before water wash, after water wash, simultaneously with water, or combination thereof. Logsdon et al. (2000) summarized the typical water and air rates for backwashing filters with different types of media.
With concurrent air scouring and water wash, the filter bed undergoes a “collapse pulsing action” under optimal air and water rates, which can be predicted according to a set of empirical equations applicable to different media gradation and water temperature (Amirtharajah 1993). Obtaining and sustaining collapse pulse action within the backwashing process is optimum for the removal of particles from the media grains. The collapse pulsing action can be described as follows: the air bubble exits the air delivery device (orifice) and expands under the weight of the media. When the air bubble expands, the media expands slightly within the vicinity of the bubble, and the bubble collapses and reforms just above its original location. This collapsing is due to the weight of the media. The bubble reforms above its original location because the media is only partially expanded. Just prior to collapsing, high local water velocities occur at the perimeter of the bubble. Simultaneous to bubble collapse, media particles rush together and collide in a violent scouring action. This creates a “pulsation” in the bed. The bubble travels on upward, expands, collapses, and re-forms again, and repeats the process several times as it passes through the bed.
Media | Density (g/cm3) | Major Constituents | Specific Surface Area (SSA) and its References | |
SSA (BET) (m2/g) | References | |||
Garnet | 3.6-4.2 | Nesosilicates | (Logsdon et al. 2002) | |
Sand | 2.6 | SiO2 | 0 | (Jerez et al. 2006) |
Anthracite | 1.6 | Carbon | 6-7 (0.2-0.4 mm size) | (Davidson et al. 1996) |
GAC | 1.3-1.5 | Carbon | 720 (<1 mm size) 900 (0.149 mm size) 928 (0.15 and 0.25 mm size) | (Gergova et al. 1993) (Oliveira et al. 2002) (Tang et al. 2004) |
Diatomaceous earth | 1.0-1.6 | SiO2, Al2O3, Fe2O3 | 27.8 (106–250 mm size) | (Al-Ghouti et al. 2003) |
Expanded clay | 1.0-1.6 | SiO2, Al2O3, Fe2O3 | 398 (10-100 m size) | (Occelli et al. 2002) |
Pumice | 0.4 | Highly vesicular texture glass | 2.1-14.2 (<63-1000 m size) | (Kitis et al. 2007) |
Characteristics of different media used for water filtration
Sketch of typical nozzle design on a filter floor
To fully clean the filter media without forming dead zones, mud balls, media encrustation, and boiled media spots, even distribution of air and water during backwashing is critical. Fundamentally, there are two types of designs for simultaneous air and water distribution, nozzles and underdrain (with integrated dual channels). Nozzles (Figure 1) usually have slot sizes in the range 0.25–0.5 mm to minimize the risk of sand penetration. Nozzle arrangement density on the floor depends on the type of nozzles and is greater than about 35 nozzles/m2 (Ratnayaka et al. 2009). However, the design of nozzle slots needs to be considered carefully to prevent fouling. There are several other underdrain systems, mostly of proprietary designs, successfully used in many parts of the world. An example is the design by Leopold (a Xylem brand) which comprises underdrain blocks (each block approximately 1 foot 1 foot 4 feet), formed from high-density polyethylene (HDPE), which snap lock together to form water resistant long laterals (Figure 2). The blocks incorporate a dual lateral design with a water recovery channel that ensures uniform distribution of concurrent air and water even over laterals up to 42 feet or 12.8 m. The blocks can be fitted with a porous HDPE IMS Cap on top that helps to eliminate the need for support gravel (Figure 2).
Sketch of typical underdrain design with dual channels (courtesy of Leopold, a Xylem brand)
Biological water and wastewater treatment processes are based on the growth of microbial communities capable of metabolizing contaminants through mediating oxidation-reduction reactions. The oxidants (electron acceptors) are normally oxygen, nitrate, perchlorate, sulfate, and Fe (III); the reductants (electron donors) are normally organic matter, trace organic compounds, ammonia, As (III), and iron (II) and Mn (II), etc. In a fixed-film biological process, biofilms are developed on media such as sand, anthracite, granular activated carbon (GAC), or membranes. A biofilm process mainly consists of two simultaneous steps, substrate diffusion and biological reaction, as illustrated in Figure 3. Electron donors and acceptors diffuse from bulk fluid into the biofilm and are metabolized by microbial cells in the biofilm, as a result of which the diffusion profiles are parabolic.
Schematic diagram of substrate diffusion in a biofilm attached to a solid substratum
Biologically active filters (BAFs) are essentially of the same physical structures as rapid gravity filters except BAFs are maximized with biological activities without backwashing the filters with chlorinated water or with no pre-chlorination. BAFs have been used for decades in North America and Europe in drinking water treatment, but have drawn more attention only recently. Regulatory and customer acceptance remain an issue because of the concern of microbial sloughing and breakthrough. A recent survey conducted by the AWWA Research Foundation indicated that 44% of the respondents believed biological processes in the drinking water industry were not accepted and 25% believed they were. Major operational concerns were breakthrough of pathogens and sloughing of bacteria (Evans et al. 2008). However, coliform bacteria were rarely observed in BAF effluents in laboratory studies, indicating that coliform organisms were eliminated by the microbial activity in the filters because of the competition for limited nutrients (Camper et al. 1985; Rollingger and Dott 1987). A pilot study demonstrated that biologically active filters reduced microbial activities in distribution systems (Characklis 1988). Furthermore, French experience indicated that removal of biodegradable materials resulted in a lesser amount of microbial presence in the distributions systems (Bourbigot et al. 1982). Comparison of physicochemical and biological treatments indicated that biological treatment limited mutagenic generation (Carraro et al. 2000).
Perhaps, slow sand filters (SSFs) were the earliest application of a biological process in drinking water treatment. The major function of a filter occurs at the surface layer (Schmutzdecke) of the sand bed which contains a zoogeal jelly in which biological activities are highest (Babbitt and Doland 1939). Full scale experience indicated NOM removal was 155 mg/L by slow sand filters (Collins et al. 1992). Coliform reduction was 2-4 log (Barrett et al. 1991), and E Coli reduction was found strongly correlated with carbon dioxide respiration in the top 2.5 cm media and protistan abundance in the top 0.5 cm Schmutzdecke (Unger and Collins 2008). With over 150 years of history, river bank filtration (RBF) showed efficient organic substance removal in full scale plants in the Netherlands and Germany (Piet and Zoeteman 1980; Sontheimer 1980). River bank filtration removed TOC by 33-86% and disinfection by-product formation potential by 30-100% at the wells in several drinking water utilities in the US (Partinoudi and Collions 2007; Weiss et al. 2003).
A previous review of biological processes in drinking water treatment summarized that a wide range of contaminants can be removed through biological oxidation and reduction of dissolved constituents including natural organic matter (NOM), ammonia, nitrate, perchlorate, and iron and manganese, where operating parameters were discussed (Bouwer and Crowe 1988). Additionally, BAFs were reported to remove trace organic compounds, halogenated organics, perchlorate, and arsenic. A BAF usually does not require the addition of other chemicals for oxidizing and removing of contaminants. It does not require close monitoring of a breakthrough point, as in conventional column adsorption processes. Some organics adsorbed in activated carbon particles can be degraded by microorganisms attached on the activated carbon, or through enzymatic reaction during normal operation and hence create some active adsorption sites (Perrotti et al. 1974; Rice and Robson 1982; Rodman et al. 1978). This process is referred as biological regeneration. The service life of activated carbon can be extended by biologically regenerating exhausted carbon. The treated water from the BAF is unlikely to produce undesirable disinfection by-products and bacteria re-growth in the water distribution system (Dussert and Van Stone 1994; Scholz and Martin 1997).
Natural organic matter (NOM), consisting of humic acid, fulvic acid, carbohydrates, and other natural compounds, is present in natural water sources and is a precursor of disinfection by products (DBPs). DBPs are compounds formed when strong oxidants such as chlorine and ozone come into contact with NOM. Epidemiological evidences supported an association between chlorinated water or trihalomethanes and bladder cancer (Cantor et al. 1987; Cantor et al. 1999; Doyle et al. 1997). The most prevalent DPBs that form as a result of contact between organic carbon and chlorine include total trihalomethanes (TTHMs) and haloacetic acids (HAA5). To mitigate public exposure to these compounds, US Environment Protection Agency (EPA) has developed regulations restricting their concentrations at all points in the distribution system. The initial legislation formed for these restrictions is known as Stage 1 Disinfectants and Disinfection Byproducts Rule. A secondary stage for the DBP rule had also been promulgated by EPA in 2006. Stage 2 Disinfectants and Disinfections Byproducts Rule was applied as an addition to the continual improvement of safety in drinking water standards in the United States. Amendments to Stage 1 DBP rule include: (a) requiring annual averages at every point in the distribution system to adhere to the predefined maximum contamination levels (MCLs), (b) escalating the sampling frequency for communities with larger populations, and (c) the utility’s distribution system must be evaluated to identify locations with elevated DBP concentrations.
To remove DBP precursors, biologically active filtration using different media was employed worldwide, usually assisted by pre-ozonation which increased assimilable organic carbon (AOC) or biodegradable organic matter (BOM) which was subsequently metabolized by biofilms in biofilters (Carlson and Amy 2001; Goel et al. 1995; Weiss et al. 2003). The design parameters are empty bed contact time (EBCT), media selection, media configuration, backwash regime selection, temperature in addition to pre-ozone doses. A summary of design parameters and media selection and observations in previous studies were provided in Table 2. Majority of the studies showed that an EBCT of 10 minutes should be used in process design to achieve 30-50% TOC removal with GAC. When combined with pre-ozonation, an EBCT of 5 minutes appeared sufficient (Hozalski et al. 1995; Laurent et al. 1999; Rittmann et al. 2002; Weiss et al. 2003).
The design of biologically active filters should be based on the achievement of one of the following criteria:
Maximal removal of DOC to reduce the formation of DBPs;
Maximal removal of BOM to minimize the risk of biological re-growth in the distribution system;
Maximal removal of potential carcinogenic ozonated by-products (OBPs) such as formaldehyde.
Direct comparison showed that the GAC/sand filter produced better performance than the anthracite/sand filter (Rittmann et al. 2002) and the GAC/Sand filter out performed anthracite/sand filter by 11% for AOC removal (Weiss et al. 2003). GAC media showed better resistance to temperature at 1-3 ºC in terms of oxalate and TOC removal compared with anthracite (Emelko et al. 2006). The BOM (10% acetate and 90% other organic matter with a maximal degradation rate less than one-tenth that of acetate) removal was reduced from 55% at 22.5 ºC to 12% at 6 ºC in a sand filter at EBCT 7.5 minutes (Hozalski et al. 1999).
Emelko et al. (2006) studied the effect of backwash and temperature on full scale biofiltration, and concluded that biodegradable organic material (BOM) removal was not influenced by backwash regimes even though some biomass expressed by phospholipid was lost during backwash with air scouring. Others also concluded that backwashing did not have noticeable impact on BOM removal because no more than 25% of biomass was washed out (Hozalski et al. 1999; Rittmann et al. 2002). However, microbial communities in the filters and during the operating condition shifts were not investigated in this study. In another study, it was found that backwashing caused changes in the relative compositions of microorganisms in a GAC biofilm in the top layer of the bed and reduced the attached bacterial abundance to 64% (Kasuga et al. 2007). The relative abundances of some terminal-restriction fragments (T-RFs) increased such as the
Nutrient levels were also shown to influence the process efficiency. In a full scale study at Daugava water treatment plant in Riga, Latvia, the process including ozonation and biofiltration was not efficient for removal of dissolved organic carbon (DOC) from waters with a high amount of humic substances likely due to phosphorus limitation (Juhna and Rubulis 2004).
Phosphorus supplementation in a pilot study decreased biofilter terminal headloss by ~15 percent relative to the control likely the result of reduced EPS formation in the filter, and decreased contaminant breakthrough relative to the control biofilter, including MIB (~75 percent less breakthrough), manganese (~90 percent less breakthrough), and DOC (~15 percent less breakthrough) (Lauderdale et al. 2011).
Pilot using filters of granular activated carbon (GAC), GAC/sand, anthracite/sand | GAC/sand filter: EBCT 5 minutes with 29% TOC removal; EBCT 10 minutes with 33% TOC removal; EBCT 15 minutes with 42% TOC removal; EBCT 20 minutes with 51% TOC removal 0.3-1.0 mg O3/mg TOC | Pre-ozonation increased Assimilable Organic Carbon (AOC) in influent, but also increased BAF effluent AOC relative to non-ozonated influent water. GAC/Sand filter was better than anthracite/sand filter by 11% for AOC removal. | (Weiss et al. 2003) |
IRWD pilot facility in Santa Ana, California, including BAF following ozonation. | EBCT from 3.5 to 9 minutes 1.0-1.8 g O3/g TOC | Up to 90% of color was removed and up to 38% DOC was removed; GAC biofilter gave better performance than anthracite. | (Rittmann et al. 2002) |
Laboratory-scale batch degradation tests | 0-7.3 mg O3/mg TOC | Biodegradability of four NOM sources was improved by ozonation in the range of 0-7.3 mg O3/mg TOC. Degradation of high molecular weight organics were more influenced by ozonation | (Goel et al. 1995) |
Laboratory-scale biologically active sand (ES 0.5 mm) filters. | EBCT from 4 to 20 minutes 2-4 mg O3/mg TOC | Ozonated NOM removal was significantly affected by the sources of the organic carbon independent of EBCT | (Hozalski et al. 1995) |
Laboratory-scale biodegradability study with Ohio River | 0.6-1.0 mg O3/mg TOC (optimal for DBP reduction) 2.0 mg O3/mg TOC (maximal for AOC) | (Shukairy et al. 1992) | |
Laboratory-scale biologically active glass beads and sand (ES 0.52 mm) filters. | EBCT 7.5 minutes 0.580.12 mg O3/mg TOC | 30% TOC removal was achieved. Perfromance was not impaired by backwash. | (Hozalski et al. 1999) |
Pilot including biologically active filters (expanded clay 0.5-2.5 mm) following ozonation. | EBCT 11-54 minutes 1.0-1.7 mg O3/mg TOC | EBCT did not have a significant impact; TOC removal 18-37% ; majority (80%) of BOM were removed; | (Melin and Odegaard 1999) |
Full scale GAC filters at River dune Water Works | EBCT 20 minutes with two stages 0.35-0.45 mg O3/mg TOC | 50% TOC removal; successive reactivation of GAC was still effective | (van der Hoek et al. 1999) |
Full scale BAC filters, St-Rose Treatment plant, Canada | EBCT 5-12 minutes | 50% BDOC removal | (Laurent et al. 1999) |
Lab scale biodegradability study | 2 mg O3/mg TOC | 40-50% DOC removal 40-60% THMFP removal 90-100% aldehydes removal | (Siddiqui et al. 1997) |
Pilot including biologically active anthracite filters following ozonation. | 0.6 mg O3/mg DOC EBCT 2-11 minutes | Maximal 9% DOC was removed at EBCT 15 minutes with 5 m/h and EBCT 7 minutes with 9.7 m/h; 80% ozone by-products were removed at EBCT 3-5 minutes | |
Lab scale fluidized GAC filter | EBCT 20 minutes 1 mg O3/mg TOC | 45% BDOC removal | (Yavich and Masten 2003) |
Pilot including biologically active anthracite filters following ozonation. | 0.6 mg O3/mg DOC EBCT 6 minutes Temperature 6-10 ºC | Up to 1.0 mg O3/mg DOC produced maximal BDOCrapid/DOC; and 0.4-0.6 mg O3/mg DOC produced maximal BDOCrapid/BDOCtotal. Cumulative 90% DOC removal at EBCT 6 minutes. | (Carlson and Amy 2001) |
Pilot including anthracite and GAC filters directly following ozonation. | EBCT 5 minutes 0.5-1.0 mg O3/mg DOC | 7-9% TOC (as UV254) removal; No difference was observed between GAC and anthracite filters due to the nature of the water | (Chaiket et al. 2002) |
Full scale including GAC filters directly following ozonation at Sweeney WTP, Wilmington, NC. | EBCT 10-60 minutes 0.5-2 mg O3/mg DOC | 10-50% DOC removal; no significant difference between lignite and bituminous GAC. | (Najm et al. 2005) |
Design parameters and findings for the removal of natural organic matter removal (NOM) in previous studies
The presence of tastes and odors in drinking water is an increasing and serious problem in the United States and the world. Some species of algae and bacteria naturally produce odorous chemicals inside their cells. Geosmin (trans-1, 10-dimethyl-trans-9-decalol) and MIB (2-methylisoborneol) are common odorous chemicals. There are no maximal contamination levels (MCLs) for MIB and Geosmin in drinking water systems according to US EPA. However, earthy and musty odors generated by Geosmin and MIB are detectable by individuals at the concentrations of 5 to 10 parts per trillion, and often result in customer complain. When large numbers of algae and bacteria flourish in a water body (an “algae bloom”), taste and odor-compound concentrations increase to levels above this threshold and cause taste and odor problems.
Biological active filtration was effectively used for the removal of Geosmin and MIB as summarized in Table 3. Since the concentrations of Geosmin and MIB encountered in drinking water systems are usually much less than that of TOC, a secondary utilization pathway was proposed as opposed to primary substrate utilization (Bouwer and Crowe 1988). Primary substrates support steady state biofilms which in turn metabolize secondary substrates such as Geosmin and MIB.
Unlike the removal of TOC, placing ozonation in front of GAC filters was not benefiting the removal of MIB and Geosmin likely due to the competition from increased AOC (Vik et al. 1988). An important design parameter is EBCT, which is usually in the range of 5-20 minutes depending on the required removal. GAC filters provided resistance to temperature variation while the removal was reduced by 24% for both Geosmin and MIB when temperature was reduced from 20 to 8C in anthracite filters (Elhadi et al. 2006). At lower temperatures (6-12 C), MIB and Geosmin removal was also reduced with expanded clay by 15% and 10%, respectively, compared to that at 15 C (Persson et al. 2007). Biodegradation of both MIB and Geosmin was determined to be a pseudo-first-order reaction, with rates influenced by the initial amount of the biofilm biomass (Ho et al. 2007). As a result, sand with a well-established biofilm taken from a 26 years old filter was capable of removing MIB and Geosmin to below detection limits after 11 days of operation while sand without an established biofilm removed 60% Geosmin and 40% MIB after 154 days of operation (McDowall et al. 2007). Four bacteria, a
Full scale study at CLCJAWA Water Treatment Plant at Lake Bluff, Illinois, which included biologically active GAC filters following ozonation. | 0.66-0.81 mg O3/mg TOC EBCT 10-20 minutes | Ozonation removed 36-65% MIB and biofiltration removed 26-46% of MIB. The biodegradability of geosmin and MIB was confirmed by a bench scale study, where 55% and 44% removal was achieved for geosmin and MIB, respectively. | (Nerenberg et al. 2000) |
Pilot study of sand filters capped with biologically active GAC. Geosmin was in the range of about 70 to 110 ng/L, | EBCT 5 minutes | 11 to 38% | (Ndiongue et al. 2006) |
EBCT 7.5 minutes | 78% geosmin removal; Geosmin was better removed than MIB. | ||
Bench-scale two 2.0 m high glass GAC/sand filters | EBCT 5.6 minutes Temperature 12-16 C | 76 to 100% geosmin removal and 47% to 100% MIB removal. The exhausted GAC initially removed less geosmin and MIB, but the removals increased over time. | (Elhadi et al. 2004) |
Bench-scale GAC/sand and anthracite/sand filters | EBCT 5.6 minutes Temperature 8 and 20 C | 60% geosmin and 40% MIB removal at 20 C in GAC filters. 36% geosmin and 16% MIB removal at 8 C in anthracite filters. | (Elhadi et al. 2006) |
Bench-scale sand filters | EBCT 15 minutes | 60% geosmin and 40% MIB with new sand after 154 days; reduced to below detection limit with sand from a 26 years old filter with a well-established biofilm | (McDowall et al. 2007) |
Pilot study of GAC and expanded clay filters | EBCT 6, 15 and 30 minutes | Exhausted GAC had adsorption capability for MIB and Geosmin. At initial 20 ng/L MIB and 20 ng/L Geosmin: 97% removal at 30 minutes EBCT; 90% removal at 15 minutes EBCT; "/>40% remocal at 6 minutes EBCT | (Persson et al. 2007) |
Bench-scale sand filters | EBCT 15 minutes 202 C | 95% removal of both MIB and geosmin with sand from an over 30 year facility | (Ho et al. 2007) |
Pilot GAC and ozone plus GAC | EBCT 21 minutes 2-5 O3 mg/L 1.5-4 TOC mg/L | TOC removal was better with ozone plus GAC; GAC was better for MIB and geosmin removal than ozone plus GAC because of the competition of TOC. GAC kept Geosmin and MIB below 10 ng/L | (Vik et al. 1988) |
Design parameters and findings for the removal of MIB and Geosmin in previous studies
Appreciable amounts of iron and manganese usually exist in ground water or lake water experiencing low dissolved oxygen levels. The US EPA set secondary MCLs for iron and manganese at 0.3 mg/L and 0.05 mg/L, respectively.
There are two valences of iron and manganese, Fe (II) and Fe (III); Mn (II) and Mn (IV). Fe (II) and Mn (II) are quite soluble than Fe (III) and Mn (IV), respectively. As summarized in a previous study, the solubility product of ferrous hydroxide was in the range of 710-13 to 4.510-21 while the solubility product of ferric hydroxide was 310-38 to 410-36 (Gayer and Woontner 1956). The solubility product of manganous hydroxide was 9.010-14 and manganese dioxide was in equilibrium with Mn(OH)4 (aq) with an equilibrium constant of 4.010-5 (Swain et al. 1975). Mn(OH)4 (aq) will be prone to adsorption during filtration. Therefore, media filtration will not be effective to remove total iron and manganese if considerable portions are at the lower valance. Physicochemical removal requires a strong oxidant injected in front of media filtration to oxidize lower valance metals to a higher valance (Equations 1 and 2) and then filtered out.
Fe (II) and Mn (II) can provide energy as electron donors for autotrophic biological reactions when oxygen is present. Biological filtration was demonstrated effective for iron and manganese removal assisted with aeration or ozonation in front of filtration (Table 4). It appeared that EBCT of 10 minutes reduced iron and manganese by 95-100% with coarse or fine sand media (Katsoyiannis and Zouboulis 2004; Lytle et al. 2007a; Štembal et al. 2005; Tekerlekopoulou et al. 2008). The use of ozone was beneficial (Pokhrel et al. 2005), but it appeared that aeration was sufficient for providing oxygen (Katsoyiannis and Zouboulis 2004; Lytle et al. 2007a; Štembal et al. 2005; Tekerlekopoulou et al. 2008).
Arsenic is a semi-metal element in the periodic table. It is odorless and tasteless. It enters drinking water supplies from natural deposits in the earth or from agricultural and industrial practices. Non-cancer effects can include thickening and discoloration of the skin, stomach pain, nausea, vomiting; diarrhea; numbness in hands and feet; partial paralysis; and blindness. Arsenic has been linked to cancer of the bladder, lungs, skin, kidney, nasal passages, liver, and prostate. US EPA set the arsenic standard for drinking water at 0.01 mg/L (10 parts per billion or ppb) to protect consumers served by public water systems subject to the effects of long-term, chronic exposure to arsenic.
Understanding the oxidation state is important for arsenic removal from drinking water. There are two oxidation states for arsenic: arsenite (As (III)) and arsenate (As (V)). Arsenite typically forms aqueous As(OH)3, As(OH)4-, and AsO2OH2-, depending on pH; dissolved arsenate forms AsO43-, HAsO42-, or H2AsO4- (Edwards 1994; Katsoyiannis et al. 2002). At 6.9 < pH < 11.5, HAsO42- is the primary species; and at 2.2 < pH < 6.9, H2AsO4- is the primary arsenate species (Edwards 1994; Katsoyiannis et al. 2002). Arsenate adsorbs to soil minerals, particularly iron oxides and hydroxides. Arsenate sorption to iron oxides peaks around pH 5-7. Arsenite tends to adsorb less strongly than arsenate. Source water containing arsenite generally requires using a strong oxidant, e.g., chlorine, chlorine dioxide, and ozone, to oxidize arsenite to arsenate which can be removed by coagulation and filtration. Arsenic (V) removal by either ferric chloride or alum was relatively insensitive to variations in source water composition below pH 8 meanwhile arsenic (III) removal by ferric chloride was less efficient and more strongly influenced by source water composition than arsenic(V) removal (Hering et al. 1997). The presence of sulfate (at pH 4 and 5) and natural organic matter (at pH 4 through 9) adversely affected the efficiency of arsenic (III) removal by ferric chloride and arsenic(III) could not even be removed by coagulation with alum (Hering et al. 1997).
Pilot GAC column with 1740 mm (height) by 450 mm (diameter) | Fe 6.4-8.4 mg/L Mn 0.93-0.99 mg/L As 14.5-27.3 g/L DOC 4.3-4.9 mg/L | EBCT 34.1 minutes 6.2-8.5 O3 mg/L | The biologically active system removed 99.8% of Fe(II), and with the ozone pretreatment, the average removal increased to 99.9% for Fe(II). | (Pokhrel et al. 2005) |
Pilot study of roughing filter with gravel and sand filter | Fe 0.09-0.44 mg/L Mn 0.18-1.83 mg/L | EBCT 30 and 60 minutes (roughing filter) EBCT 5 minutes (sand filter) | Iron and manganese removal efficiencies were between 85% and 95%. | (Pacini et al. 2005) |
Pilot filter (1 m high polystyrene Beads 3-4 mm) | Fe 2.8 mg/L | EBCT 7.3 minutes | Fe(II) was microbiologically oxidized to Fe(III) precipitated on the filter bed. | (Katsoyiannis and Zouboulis 2004) |
Bench-scale sand filter | Mn 0.86-1.83 mg/L | 90% Mn removal | (Burger et al. 2008) | |
Pilot trickling filter (1.9 mm sand) | Mn 0.6–2.0 mg/L | EBCT 9 minutes | Close to 100% Mn removal | (Tekerlekopoulou et al. 2008) |
Pilot sand filter (190 cm high, 0.5-2.0 mm sand) | Fe 0-2.45 mg/L Mn 0.1-1.06 mg/L NH4-N 0.02-2.62 mg/L | EBCT 4.75-10.4 minutes | 95% Fe, Mn, and NH4-N removal | (Štembal et al. 2005) |
Pilot dual media filter (20” anthracite over 10” sand) | As 372 g/L NH4-N 1.15 mg/L Fe 2289 114 g/L | EBCT 9.4 minutes | Reduced Fe to less than 25 g/L | (Lytle et al. 2007a) |
Design parameters and findings for the removal of iron and manganese in previous studies
Pilot dual media filter (20” anthracite over 10” sand) | As 372 g/L NH4-N 1.15 mg/L Fe 2289 114 g/L | EBCT 9.4 minutes | Reduced to less than 10 g/L; Majority of As in the effluent was particular. | (Lytle et al. 2007a) |
Pilot anthracite/sand filters | As 20-46 g/L | EBCT 10 minutes | Reduced to less than 10 g/L | (Lytle et al. 2007b) |
Pilot GAC column with 1740 mm (Height) by 450 mm (Diameter). | Fe 6.4-8.4 mg/L Mn 0.93-0.99 mg/L As 14.5-27.3 g/L DOC 4.3-4.9 mg/L | EBCT 34.1 minutes 6.2-8.5 O3 mg/L | 97% without ozonation; 99% with pre-ozonation | (Pokhrel et al. 2005) |
Pilot filter (1 m high polystyrene Beads 3-4 mm) | As 50–200 μg/L | EBCT 7.3 minutes | Reduced to less than 10 μg/L | (Katsoyiannis and Zouboulis 2004) |
Pilot filter (1 m high polystyrene Beads 3-4 mm) | As 40–50 μg/L DO 2.7 mg/L | EBCT 7.3 minutes | 80% As removal | (Katsoyiannis et al. 2002) |
Pilot filter with at least 0. 66 m sand | As(III) 30-200 g/L Fe(II) 0.5-1.5 mg/L Mn(II) 0.6-2.0 mg/L | EBCT 7.9 minutes | 95% As removal | (Liu et al. 2010) |
Pilot slow sand filter (sand 0.45-0.55 mm mixed with iron fillings) | As 50 μg/L | EBCT 4-5 hours 0.023 m/h | Columns containing filtration sand only removed As <11%; all iron/sand columns achieved greater than 92% removal. | (Gottinger 2010) |
Pilot slow sand filter (90 cm, sand 0.25-2 mm) | As 10–35 μg/L | EBCT 4.25 hours | Reduced to less than 5 μg/L if Fe/As feed ratio was 40 | (Pokhrel and Viraraghavan 2009) |
Pilot GAC filter (100 cm, GAC 2-4 mm) | As(III) 25 mg/L Fe(II) 10 mg/L Mn(II) 2 mg/L | EBCT 6 hours | "/>99% As removal 80% Fe removal 95% Mn removal | (Mondal et al. 2008) |
Design parameters and findings for the removal of arsenic in previous studies
The current treatment options include activated alumina, iron oxide coated sand, greensand, reverse osmosis, ion exchange, and electrodialysis in addition to coagulation (Edwards 1994).
Similar to Fe (II) and Mn (II), arsenite can provide energy as an electron donor for autotrophic biological reactions when oxygen is present. Biological filtration was shown effective for arsenite removal assisted with aeration or ozonation in front of filtration (Table 5). Various media including sand, anthracite, GAC, and polystyrene beads were used for arsenic removal. Generally an EBCT of 10 minutes is required to reduce arsenic from up to 100 g/L down to less than 10 g/L. Due to the strong affinity of arsenate to ferric oxide, feeding ferric in the influent increased the removal efficiency with an increasing Fe/As ratio (Pokhrel and Viraraghavan 2009). A study found filtration columns containing mixture of sand and iron fillings improved removal and were capable of reducing arsenic from 50 to well below 10μg/L with an average of 92% removal (Gottinger 2010).
To improve the removal efficiency, immobilizing whole bacterial cells has attracted more research interest in recent years.
Although there is no ammonia drinking water standard in the United States, the European community has established a maximum limit of approximately 0.5 mg/L and a guide level of 0.05 mg/L (EU Council 1980). Although there are no immediate indications that ammonia will become regulated within the United States, there are benefits for utilities to reduce the amount of ammonia that is able to enter a distribution system. The presence of ammonia in drinking water distribution systems has been correlated to increased biological activity, corrosion, formation of nitrite and nitrates, and adverse impacts on taste and odor (AWWA 2006). In addition, the presence of ammonia can interfere with the effectiveness of some water treatment processes including biological manganese removal as ammonia removal must be achieved before manganese removal due to the fact that the oxidation potential for nitrification is lower than manganese oxidation (McGovern and Nagy 2010).
Nitrification can be achieved in different ways, but may be most cost effectively accomplished by employing biofiltration (Table 6). The effectiveness of biological ammonia oxidation treatment to reduce source water ammonia levels is dependent on a number of source water and engineering design factors including temperature, dissolved oxygen, TOC, pH, biomass quantity and population, media type, and surface area, as well as hydraulic loading rate and contact time (Zhang et al. 2009). Factors affecting nitrification occurrence, nitrification impacts on water quality and corrosion, and nitrification monitoring and control methods were reviewed previously (Zhang et al. 2009). Arrhenius coefficient was 1.12 without acclimation and 1.06 with acclimation (Andersson et al. 2001). At temperature less than 4 C, nitrification seemed un-sustained and feeding low temperature culture (psychrophiles) seemed necessary (Andersson et al. 2001). However, in fixed-film biofilters, the impact of temperature on nitrification rate was less significant than that predicted by the van’t Hoff-Arrhenius equation, and a temperature increment at 20 ºC resulted in a nitrification rate increase of 1.108% per degree and 4.275% per degree under DO and ammonia limiting conditions, respectively (Zhu and Chen 2002).
Pilot sand filter (190 cm high, 0.5-2.0 mm sand) | Fe 0-2.45 mg/L Mn 0.1-1.06 mg/L NH4-N 0.02-2.62 mg/L | EBCT 4.75-10.4 minutes | 95% Fe, Mn, and NH4-N removal | (Štembal et al. 2005) |
Full scale and pilot scale GAC filters at St Rose WTP at Laval, Canada | NH4-N < 1 mg/L | EBCT up to 28 minutes | EBCT 5 minutes seems suitable for removing ammonia; | (Andersson et al. 2001) |
Pilot gravel filter (180 cm high, 6-12 mm and 12-25 mm gravel) | NH4-N < 1 mg/L 26 C | EBCT 4-23 minutes | 70-80% at EBCT 7.8 minutes; "/>90% at EBCT 10 minutes | (Forster 1974) |
Pilot aerated GAC filter (85 cm high) | NH4-N 2.88 mg/L | EBCT 10 minutes | 95% ammonia removal | (Rogalla et al. 1990) |
Pilot dual media filter (20” anthracite over 10” sand) | NH4-N <1.7 mg/L | EBCT 9.4 minutes | Close to 100% ammonia removal | (Lytle et al. 2007a) |
Design parameters and findings for the removal of ammonia in previous studies
European experience on nitrification was reviewed for trickling filters, up-flow fluidized bed filters, rapid sand filters, and GAC filters (Rittmann and Snoeyink 1984). In a pilot trickling filter operated at 2.4 m/h with 2 m gravel media, ammonia removal was 80% at 20 ºC; 78% at 15 ºC; 67% at 10 ºC; and 50% at 5 ºC. In fluidized filters, nearly 100% removal was achieved as long as the fluidized solids were at least 30% by volume from 4-20 ºC. Nearly 100% nitrification was achieved using rapid sand filters at Mulheim where raw water contained 1 mg/L ammonia nitrogen. Complete nitrification was achieved with GAC filters at 10 m/h with an EBCT of 10 minutes.
Phylogenetic tree of AOB based on multiple alignment of 55 nearly full-length AOB 16S rDNA sequences. Abbreviations are Nm for
Nitrification is a two-step process: ammonia oxidation and nitrite oxidation. The bacterial genera associated with ammonia oxidation are named as ammonia oxidizing bacteria (AOB) and the bacterial genera associated with nitrite oxidation are named as nitrite oxidizing bacteria (NOB). Both AOB and NOB are autotrophic bacteria using carbon dioxide for cellular synthesis under aerobic conditions. Phylogenetic trees for AOB and NOB were summarized elsewhere (Regan 2001) as shown in Figures 4 and 5, respectively. Kinetic parameters including specific substrate utilization rate, half saturation constant, yield, etc of
Phylogenetic tree of NOB based on a multiple alignment of 40 NOB 16S rDNA sequences. Abbreviations are Nb for
With the application of nitrate containing fertilizers and consumption of animal products, more nitrate is discharged into rivers and lakes, which may cause eutrophication and elevated levels of nitrate in ground water and surface water. Although nitrate was not identified as a carcinogen to laboratory animals, methaemoglobinaemia forms as a consequence of the reaction of nitrite (reduced from nitrate in human bodies) with haemoglobin in the human red blood cells to form methaemoglobin, which binds oxygen tightly and does not release it, therefore blocks oxygen transport (WHO 2008). The maximal contamination level in drinking water was 10 mg/L nitrate nitrogen in US, Japan, and Korea. The European Union countries set the standard for nitrate nitrogen at 11.3 mg/L. World Health Organization recommends 11.3 mg/L nitrate nitrogen to protect against methaemoglobinaemia in bottle-fed infants. Traditionally, nitrate removal was achieved by biological denitrification, ion-exchange, adsorption, chemical reduction, and membrane separation such as reverse osmosis. Ion exchange and membrane processes were often applied for high purity water treatment, which will generate concentrated nitrate reject (usually need additional treatment) from resin or membranes. Biological treatment processes were widely used for wastewater and drinking water treatment.
Denitrification filters are a unique type of biologically active filter where an external carbon source is usually added to the filter to provide a food source to anoxic biology and facilitate the reduction of nitrates in the filter. As a result, a dedicated anoxic biology is likely developed in the filter.
It is generally recognized that denitrification is carried out in the following steps with the aid of various enzymes produced during the process in the form of intracellular and extracellular polymeric substances, i.e., nitrate reductase (Nar), nitrite reductase (Nir), nitric oxide reductase (Nor), and nitrous oxide reductase (Nos) (Rittmann and McCarthy 2001).
Denitrifiers are a group of heterotrophic bacteria and are phylogenetically diverse. They belong to over 50 genera and fall into all major physiological groups (Zumft 1992; Zumft 1997). Furthermore, microorganisms fed with different carbon sources showed distinct features. A report demonstrated that the metabolic profiles obtained from potential denitrification rates with 10 electron donors were altered with their preferences for certain compounds after supplementing methanol or ethanol, and that methanol had the greater impact (Hallin et al. 2006).
Denitrifying bacteria fed with methanol were recognized as methylotrophs. Fluorescence in situ hybridization (FISH) combined with microautoradiography (MAR) revealed that α-
A study showed that methanol-utilizing organisms can not use acetate or sugar (at least not immediately). Adding alternative carbon sources, i.e., acetate or sugar, will not result in an immediate improvement in denitrification (Dold et al. 2008). However, the substrate uptake rate (max) and specific denitrification rate (SDNR), measured by feeding ethanol to methanol-utilizers, indicated that ethanol was also used essentially as easily and at a similar rate to methanol by the methanol-utilizers (Dold et al. 2008).
The stoichiometric and kinetic information for different carbon sources commonly used in a denitrification filter were studied previously and selected parameters are summarized elsewhere (Omnis-Hayden and Gu 2008). Table 7 summarized findings and operating parameters in previous studies. The important design parameters are EBCT, C/N ratio (the ratio of external carbon to nitrate nitrogen), pH and temperature.
Pilot up-flow fluidized sand filter (1.2 m deep 0.6-0.8 mm sand) | NO3-N 27 mg/L | EBCT 2.57 minutes C/N 1.3 (propionic acid) | Close to 100% NO3-N removal | (Holló and Czakó 1987) |
Pilot Rotating Biological Contactors (RBC) (75 m2) | NO3-N 40-250 mg/L pH 7 and 202 ºC | NO3-N loading: 76 mg/m2•h 490 mg/m2•h Acetic acid as a carbon source | 99% (for 76 mg/m2•h) and 87% (490 mg/m2•h) The optimum C/N ratio 4.3 | (Mohseni-Bandpi et al. 1999) |
Bench-scale membrane reactors. | NO3-N 100-200 mg/L 201 ºC | C/N ratio 2-4 (ethanol) | 99% removal with denitrification rates up to 1.23 g NO3-N/m2 •d | (Fuchs et al. 1997) |
Bench-scale up-flow fixed-bed reactor | NO3-N 20 mg/L 251 ºC | EBCT 48 minutes Molasses | 85% NO3-N removal with an optimum C/N ratio 2 | (Ueda et al. 2006) |
Bench-scale experiments. Calcium tartrate (4% w/w) co-immobilized in alginate beads with microorganisms | NO3-N 110 mg/L | 99% NO3-N removal with NO2-N residual (2-4 mg/L) higher than expected | (Liu et al. 2003) | |
Bench-scale carbon packed fixed-biofilm bed reactor, inoculated with | NO3-N 200 mg/L | EBCT 78 minutes C/N ratio 2-4 (ethanol) | 90% NO3-N removal | (Pekdemir et al. 1998) |
Bench scale packed Pall Rings (16 mm diameter and length) bed | NO3-N 50 mg/L pH 7.5-7.8 | EBCT 30 minutes COD/N ratio 4 (ethanol) | NO3-N 0.9 mg/L; NO2-N 0.7 mg/L; COD 34 mg/L | (Dahab and Kalagiri 1996) |
Bench scale packed Pall Rings (16 mm diameter and length) bed | NO3-N 50 mg/L pH 7 and 20 ºC | EBCT 30 minutes COD/N ratio 4 (ethanol) | NO3-N 2.4 mg/L; NO2-N 0.8 mg/L; COD 18 mg/L | (Woodbury and Dahab 2001) |
Full scale packed polypropylene beads (3.8 mm and 5 mm diameter) bed | NO3-N 20 mg/L pH 7.2 and 13-18 ºC | EBCT 66 minutes COD/N ratio 5.3 (corn syrup) | NO3-N 5.0 mg/L; NO2-N 1.7 mg/L; COD 20 mg/L | (Silverstein and Carlson 1999) |
Pilot ceramic media bed (1.5 m high and 0.3 m diameter) | NO3-N 68 mg/L pH 7-7.5 and 15-20 ºC | EBCT 72 minutes COD/N ratio 4.3 (ethanol) | NO3-N <4 mg/L; NO2-N<3 mg/L; COD 10 mg/L | (Moreno et al. 2005) |
Bench scale PVC/GAC beads bed (88 cm high and 12 cm diameter) | NO3-N 45 mg/L and 20 ºC | EBCT 306 minutes COD/N ratio 5.5 (acetate) | NO3-N 5 mg/L; NO2-N<0.5 mg/L; COD 60 mg/L | (Vrtovsek and Ros 2006) |
Bench scale moving bed (Kaldnes K1) | NO3-N 60 mg/L pH "/>7 and 20 ºC | EBCT 54 minutes COD/N ratio 13.1 (acetate) | NO3-N 4.7 mg/L; NO2-N<0.25 mg/L; COD 400 mg/L | (Welander and Mattiasson 2003) |
Pilot 900 L moving bed reactor (carrier, Natrix 6/6C, ANOX AB, Lund) | NO3-N 800 mg/L pH 7.8 and 17 ºC | EBCT 17 hours COD/N ratio 4 (acetate) | NO3-N ~0 mg/L; NO2-N ~0 mg/L | (Welander et al. 1998) |
Pilot moving plastic media bed | NO3-N 13 mg/L NO2-N 0.5 mg/L 7-10 ºC | EBCT 26 minutes COD/N ratio 4 (acetate) | NO3-N 2.0 mg/L; NO2-N 0.9 mg/L; COD 50 mg/L | (Rusten et al. 1995) |
Membrane bioreactor with hollow-fibers (1.1 mm inner diameter, 1.4 mm external diameter, 0.38m length) | NO3-N 200 mg/L pH 7.2 | EBCT 26 minutes COD/N ratio 3 (methanol) | NO3-N 5.7 mg/L; NO2-N 0.02 mg/L; COD 70 mg/L | (Ergas and Rheinheimer 2004) |
Pilot-scale fixed-bed bioreactors packed with sand or plastics. | ClO4− 77 μg/L | EBCT 15 minutes Acetic acid as a carbon source | Reduced to <4 μg/L | (Min et al. 2004) |
Six-month pilot at the Castaic Lake Water Agency, Santa Clarita, CA, using fixed-bed bioreactors | 18-20 mg NO3-N/L 17-20 μg ClO4−/L | EBCT 15 minutes Acetic acid as a carbon source | Reduced to less than detection limit | (Brown et al. 2005) |
Bench-scale Granular Activated Carbon (GAC) bioreactor | ClO4− 50 μg/L | EBCT 25 minutes Acetic acid as a carbon source | Reduced to less than detection limit | (Brown et al. 2002) |
Bench-scale hydrogen permeable membrane fixed film reactor | 1,000 mg NO3-N/L and/or 500 mg ClO4−/L | EBCT 48 hours 72 cm2 membrane surface | 80% nitrate removal and 60% ClO4− removal (no nitrate); the presence of nitrate reduced ClO4− removal. | (Chung et al. 2007) |
Bench-scale hydrogen permeable membrane fixed film reactor | 12.5 mg NO3-N/L 9.4 μg ClO4−/L | EBCT 55 minutes 83.6 cm2 membrane surface | 99.5% reduction of 0.21 mg NO3-N/cm2•d and 3.4 g ClO4/cm2•d | (Ziv-El and Rittmann 2009) |
Pilot up-flow packed bed reactors (plastic media) | NO3-N 325 mg/L ClO4− 6.37 mg/L | EBCT 15 h pH of 7.0 | Completely removed perchlorate and nitrate with up to 10% salinity | (Chung et al. 2010) |
Pilot anthracite filter (0.31 m deep and 1.0 mm ES) | NO3-N 2 mg/L ClO4− 50 μg/L | EBCT 47 minutes | Reduced to <2 μg/L with temperature as low as 10 ºC | (Dugan et al. 2009) |
Design parameters and findings for the removal of nitrate and perchlorate in previous studies
Perchlorate occurs in water due to natural presence or manufacturing for ammonia perchlorate (Srinivasan and Sorial 2009). Being a strong oxidant, perchlorate was used as solids propellants for rockets and missiles or used for fireworks. The US EPA is not currently regulating perchlorate in drinking water but already placed it in the contaminant candidate list.
Table 7 also summarized findings and operating parameters in previous studies for pechlorate removal. Usually perchlorate is removed simultaneously with nitrate. Because the standard oxidation potential of the perchlorate/chloride pair (1.28 V) is much higher than nitrate/N2 pair (0.75 V), perchlorate is reduced preferentially, and can be reduced down to less than 4 μg/L with an EBCT at least 15 minutes. High reduction rates of nitrate and perchlorate occurred in a synthetic high-strength salt medium 20 g/L (~2%) NaCl, while 40 g/L NaCl slowed reduction by 40% or more (Chung et al. 2010).
Similar to nitrate removal, biological processes are still cost effective for perchlorate removal. In an anoxic environment, perchlorate is reduced to chloride at the expense of an external carbon source.
While BAFs are playing an important role in contaminant removal from water sources, understanding the process design parameters such as EBCT, media selection, backwash velocity, pH, temperature, oxygen demand, pre-oxidation requirement, inhibiting metal elements, etc, is important in that it will provide insights on treatment process control, pathogenic impacts, disinfection by-product control, and the potential to improve treatment efficiencies.
An EBCT of 10 minutes is generally recommended for the removal of TOC, MIB and geosmin, iron and manganese, arsenic, and ammonia. At least 15 minutes are generally required for the removal of nitrate and perchlorate. Backwash was found not influencing the process performance. A GAC/sand filter produced better performance than an anthracite/sand filter for the removal of NOM and taste and odor compounds, and the GAC/Sand filter out performed the anthracite/sand filter by 11% for AOC removal and showed better resistance to temperature at 1-3 ºC. Unlike the situation with NOM, placing ozonation in front of GAC filters was not benefitting the removal of MIB and geosmin. Simple aeration is sufficient for providing oxygen for the removal of ammonia, and iron and manganese.
Effective microbial adhesion and immobilization is essential for biofilm activities, and still drives further research on physicochemical (for example roughing, grafting, coating, etc) and biological (inoculated with selected species) approach in BAFs. To improve the process efficiency, dedicated microbial species targeting specific contaminants are usually desired. However, it is challenging and presents significant scientific and engineering opportunities to select microbial communities in biofilms specifically adapted to targeted contaminants. Besides currently employed media, alternative cost effective media are always interested, especially the ones from waste materials and engineered with specific surface properties.
The development of the coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, triggered a global scenario of socio-economic catastrophe and psychological anguish. Many psychological problems and essential mental health consequences, including stress, anxiety, depression, frustration, uncertainty during the COVID-19 outbreak, emerged progressively.
In response to this dire circumstance, specific public policies were introduced by governments globally. These measures include self-quarantine, isolation, and social distancing. Implementing these policies caused the closure of educational institutes, offices, workplaces, public places, and social gathering restrictions. People were advised to stay at home and follow advice proposed by the WHO to protect themselves and others from the spread of COVID- 19. The strict self-quarantine order (lockdown) has kept the entire population in selected jurisdictions in isolation, with a significant impact on people’s life [1]. Although social activities have been restricted in most countries, almost all non-essential individual movements were prohibited due to quarantine, while the local hospitals received suddenly thousands of critically ill COVID-19 patients and were forced to implement their emergency protocols.
In this context, the general population and most of the front-line healthcare workers became vulnerable to the emotional impact of COVID-19 infection due to both the pandemic and its consequences worldwide. The modern society, in which everyone can travel and communicate quickly, has seldom been subjected to the present social isolation and constraints associated with feelings of dissatisfaction and uncertainty. This unusual scenario surrounding the COVID-19 epidemic illustrates that people are fundamentally and emotionally unprepared for the negative impacts of biological disasters, which immediately highlight how everyone may be vulnerable and helpless.
There is a constant update about the disease available on news channels and over the Internet, highlighting the number of individuals affected and deaths due to COVID-19. Also, inadequate knowledge and overwhelming news may contribute to fear and anxiety among the public. People at large may experience disappointment, irritability, and boredom under isolation measures [2]. The communication technologies’ facilities and transmission of inaccurate or sensational information may increase unacceptable social reactions such as aggression and rage [3].
The psychological reactions to the COVID-19 pandemic may vary from panic behavior or collective hysteria to pervasive feelings of hopelessness and desperation associated with adverse outcomes, including suicidal behavior [4]. Significantly, other health measures may be compromised by abnormally elevated anxiety. According to the social role invested, the security measures adopted in managing the pandemic had different consequences on individuals. Suspected and confirmed COVID-19 cases are likely to experience anxiety, loneliness, depression, denial, insomnia, and fear [5]. Health authorities’ strict quarantine policy and mandatory contact tracing policy may lead to social rejection, discrimination, financial loss, and stigmatization. Medical health workers are first-line fighters treating patients with COVID-19. They are a unique subset of exposed individuals as they are equipped with the resources and knowledge to address the dangers imposed by a pandemic. Despite this, they are not impervious to the detrimental impact of the pandemic on their psychological health and behavior. During the challenging times of a pandemic, healthcare workers, the most exposed group, are vulnerable to psychological challenges. Every day, they face a high risk of being infected and are exposed to prolonged and distressing work shifts to meet health requirements. This has placed healthcare workers around the world in an unprecedented situation.
This epidemic, as well as the public health measures put in place to slow it down, has drastically altered people’s lifestyles and is believed to pose a threat to their physical and mental health. This significant health catastrophe is impacting numerous countries, with high rates of transmission and death, and extensive outbreaks and mortality are linked to negative mental health consequences.
Strasner (1987) defined fear as “an emotional reaction (rational or irrational) to an object (animate or inanimate) or event that is associated with increased risk of danger and also unpleasantness, agitation and a desire to hide, flee or seek protection.” Phobia, on the other hand, is characterized by intense, severe, and persistent fear. Such fears lead to persistent efforts to avoid (flee) the source of distress, often by undertaking a number of “safety behaviors”. As a new infection with the potential for loss of life, COVID-19 has provoked legitimate fears. However, in a minority of subjects, this has also led to extreme restrictions, which could be characterized as “coronaphobia” [6]. Adequate communication that engenders a realistic appraisal of the risks, along with clear statements of behaviors that can reduce transmission, have been effective to control these fears, while also preventing viral transmission.
SARS-CoV2 infected patients experienced a high degree of fear stemming from uncertainity of illness outcome, inability to communicate with family members, lack of complete knowledge of the disease process and the stigma associated with being infected with the virus. Morever, the fear of isolation, discrimination and stigmatization was also noted in this group [7]. Those patients suspected to have SARS-CoV2 infection are also prey to the terror of having the disease, advancement of disease, precariousness of healthcare delivery and quarantine as per government regulations. Besides, even patients suffering from other illnesses displayed features of trepidation of inability to receive proper treatment due to overwhelmed healthcare resources. Patients with chronic diseases like diabetes, TB, and heart failure, for example, were found to avoid seeking medical treatment because they were afraid of contracting the illness from others in a healthcare facility, adding to fear of contagion. The type of chronic disease is a significant predictor of fear with higher levels noted among those diagnosed with autoimmune diseases or receiving immunosuppressants [8].
Despite being the highest risk exposure group, healthcare workers have been found to experience less fear compared to the patient population as well as the general public. In a study conducted in India it was observed that a mere 18.3% healthcare workers reported of fear of contracting the infection. Most healthcare workers displayed concern and fear of transmitting the infection to their families and loved ones, especially to their children and the elderly [9]. Higher levels of fear were present in lab technicians, X-ray technicians and nurses while physicians were found to have the lowest level of fear. A possible explanation of this is that technicians and nurse have more exposure to contracting the illness during sampling, testing and medication of patients [10]. Non-medical healthcare workers were assessed to have lowest fear quotient juxtaposed to medical healthcare workers who are usually in direct contact with SARS-CoV2 infected patients and have a better understanding of the disease and its mortality [11]. Alike patients, healthcare workers are not immune to the fear of social marginalization. Assault on doctors, eviction of resident physicians from their rented houses, and fear of infection from healthcare workers have all been reported [12, 13]. These factors alongwith the shortage of sufficient protective equipment, absence of effective therapy and the virus’s high infectivity rate might possibly play a role in their fearfulness of coronavirus pandemic. The presence of fear among HCWs poses a significant threat to their psychological adjustment skills and impede delivery of optimum care to patients.
Claude Bernard noted that the maintenance of life is critically dependent on keeping our internal milieu constant in the face of a changing environment. In 1956, Selye coined the term “stress” to represent the effects of anything that seriously threatens homeostasis. The actual or perceived threat to an organism is referred to as the “stressor” and the response to the stressor is called the “stress response.” Although stress responses evolved as adaptive processes, Selye observed that severe, prolonged stress responses might lead to tissue damage and disease. During pandemics, the victims and at-risk groups tend to experience stress. As a consequence of the pandemic, even the health professionals who were overworked suffered high level of psychophysical stress. Various tools and questionnaires have been devised to measure stress levels in individuals. The most commonly used are DASS-21 and SAS. Studies have shown that psychological stress can affect the immune system through neuroendocrine pathways [14].
The increasing number of confirmed cases, a lack of knowledge of COVID-19, the rapid transmission rate, infection fears, lockdown conditions, difficulties in routine medical treatments, shortages of human resources in hospitals, insufficient psychological preparation, separation from loved ones, financial loss, stigma, loss of freedom, and uncertainty over illness status act as significant stressors for patients. These patients often display features of distress such as feeling nervous, worried in certain situations of panic, tendency to overreact, inability to control things in life, inability to overcome difficulties in daily life, and coping with things. The early months of pandemic had the higher reported prevalence of stress with a significant proportion of individuals reporting being upset due to things happening unexpectedly, inability to overcome difficulties in daily life, and anger at things being outside their control. Among suspected patients, those with history of travel to areas with ongoing community transmission were found to display greater stress [15]. Patients who are kept in isolation and quarantine experience significant levels stress as well [2]. It is expected that individuals may resort to social media during corona-led social distancing for stress relief and with the aim of accessing entertaining content, such as movies, comedies, and communication with family and friends, but frequent media exposure may itself be a source of psychological stress for these patients [16, 17]. Distress and panic during pandemics can propagate and promote misinformation in various ways along with increased digital screen time and unhealthy use of technology. Amid this, there has been a hidden epidemic of “information” that makes COVID-19 stand out as a “digital infodemic” from the earlier outbreaks. Misinformation and fake news are invariable accompaniments to this “information pollution” which can add to existent superior stress levels in the population [18]. Unfortunately, even immunocompromised and chronic disease patients are under tremendous psychological and physical stress in the face of this large-scale infectious public health crisis due to lack of proper healthcare infrastructure and treatment [19, 20, 21].
Amid the ongoing unforeseen situation of coronavirus disease 2019 (COVID-19) pandemic, health care workers of multiple disciplines have been predisposed to a considerable amount of stress. A statistically significant association was found between perceived stress and female sex, designation (postgraduate trainees), work hours ≥6 hours/day, COVID-19 positive ward duties and staying with family due to potential risk of transmission to family members [22]. Disturbed sleep time and schedules due to rotational duties exhaust these HCWs both physically and mentally. The physical discomfort and occupational dermatoses associated with the use of PPE have also been shown to be related to greater levels of stress symptoms [23]. Practicing new methods of a discipline different than what he/she has mastered over the years, long hours of working in extreme conditions under PPE kit discomfort, long duty hours with no food/water/urination allowed during the time of duty with PPE, and irritation and physical strain in PPE during the summer season have been constant precipitators of stress among the doctors of all the disciplines working as frontline warriors [22]. With a scarcity of resources and an ever increasing number of patients, life-saving decisions have to be made. Making such decisions amid intense work pressure leads to added stress within emergency departments as well as during after-duty hours [24]. All doctors are susceptible to developing higher stress irrespective of their discipline during the COVID-19 pandemic, and this is further precipitated by their quarantined living conditions [22, 25]. Older HCWs demonstrate a high level of psychological distress, possibly as a result of increased complications of COVID-19 with increasing age and also because older people may have health issues making them more prone to infection with its complications [26]. In conclusion, HCWs are exposed to a protracted source of distress which may exceed their individual coping skills and lead to further chronic psychological problems.
Anxiety is an emotion characterized by the feeling of tension, worried thoughts and physical changes such as increased heart rate, according to the American psychological association. Since the onset of pandemic, many studies have been conducted to ascertain the prevalence of COVID-19 related anxiety among various sectors of the population. Conventional tools utilized to assess anxiety are:
Depression, anxiety and stress scale-21 (DASS-21)
Self-rating anxiety scale (SAS)
Generalized anxiety disorder scale (GAD-7)
Chinese version of GAD-7 scale
Hospital anxiety and depression scale (HADS)
The magnitude of anxiety varies across the country of origin of study, measurement tool used and size of the studied sample.
Patients suspected or diagnosed with SARS-CoV2 infection experience significant anxiety related to disease and external factors associated with having the infection. Several patient related factors influence the vulnerability to anxiety such as sex, marital status, symptoms and sleep quality, among others [27, 28, 29, 30]. There are many patient-related factors associated with anxiety such as:
Female gender
Older age
Divorce/bereavement
Married
Residing with family
Need of oxygen inhalation
History of fever
Two or more symptoms of infection
Suspected/confirmed infection
Poor sleep quality
Quarantine/isolation
The external factors include spread of misinformation by media, government regulations such as quarantine procedure, lockdown and travel restrictions, and social stigmatization. The current pandemic underlined the vital role played by media in dissemination of information. Ideally, media should ensure that crisis communication helps in dispelling fear and uncertainty, but in the recent pandemic it played a reverse role in spreading anxiety and panic behavior [31, 32, 33]. The endless newsfeeds related to COVID-19 infection and death rates considerably increased the adverse psychological outcomes in general population as well as patients. The spread of misleading narratives, provoking controversies and advertisement of unapproved therapies resulted in widespread fear, confusion and panic with serious mental health consequences such as anxiety [34, 35]. Quarantine and isolation of suspected and confirmed cases as per government regulations also led to abnormally increased anxiety. Additional measures like lockdown and travel restrictions also contribute to feeling of uncertainty, perceived lower social support, separation from loved ones, loss of freedom and boredom [2]. Notably, individuals with a previous health disorder experienced worsening of their symptoms due to anxiety.
Healthcare workers are directly involved in diagnosis, treatment and care of SARS-CoV2 patients and therefore experience the highest share of mental health issues compared to the other groups in the population. The prevalence of anxiety among healthcare workers is high and has been assessed to be between 23.2% to 30.5% [36, 37]. Several factors have been attributed to cause anxiety in healthcare workers. These include increased workload, inadequate PPE, inadequate isolation precautions, risk of contracting disease, high infectivity and mortality of disease, burnout, lack of clinical experience, young age, chronic health illness and prior history of mental health disorders [31, 32, 33]. Socio-demographic variables play a key role in determining the level of anxiety in healthcare workers with higher prevalence among those above the age 40, females, unmarried individuals and presence of offspring [38]. Anxiety in this group manifests as palpitations, tremors, dryness of mouth, feeling scared without any reason and tendency to panic [15]. These symptoms are more prevalent in workers who were assigned duties in COVID designated areas and those who encountered SARS-CoV2 suspected or confirmed patients without adequate protection [5]. Nurses have unfavorable mental health outcomes among the healthcare staff since they usually spend more time caring for patients than any other group of HCWs [11, 36, 39]. The existence of anxiety is associated with reduced performance and fatigue in healthcare workers and impedes optimum healthcare delivery to the population.
Depression is one of the five most incapacitating illnesses, and by 2030, it is expected to be one of the major issues in industrialized countries. It’s a common reaction to a rapid deterioration in living conditions, which involves isolation and uncertainty. When people are subjected to unpredictable circumstances, they feel helpless and unmotivated, which can lead to sadness [40]. During past epidemic outbreaks (SARS and Ebola), rates of depression in the general population have been reported with a prevalence approaching even 73.10 percent [41]. Past epidemics were more quickly contained, and infection rates were lower despite greater fatality rates, which might explain why depressive symptoms were less common [42]. Furthermore, during the SARS pandemic in Canada, Hawryluck et al. discovered that the length and unpredictability of the lockdown contributed to greater levels of depression [43]. As a result, the present global lockdown measures might explain the increased incidence of depressive symptoms reported during the COVID-19 epidemic. According to a research that looked at 69 million health data from over 62,000 persons diagnosed with COVID-19, 6% of COVID-19 patients suffered mental health problems including sadness and anxiety within three months of diagnosis, compared to 3.4 percent of non-COVID-19 patients [44]. The addition of a psychological burden to an already devastating physical burden affects the prognosis in a number of ways, with long-term consequences. Furthermore, those who are depressed are less likely to seek treatment for physical or mental problems; as a result, depression, like anxiety, can act as a barrier to reasonable medical and mental health interventions during a pandemic.
Earlier outbreaks such as SARS and MERS have displayed the presence of depressed mood among those with acute infection [45]. The COVID-19 pandemic is no different. The reported prevalence of depression at 6% in COVID-19 patients is higher to general population [44]. Females are more likely to experience depression than males [46]. According to one study, nearly one-fourth patients had intended to commit suicide or harm oneself [27]. Individuals who were married or underwent a divorce exhibited greater levels of depression than single individuals [27]. This could in part be explained by worries for one’s family and grief of separation respectively. Having a family member with confirmed COVID-19, and having two current physical symptoms are independent risk factors for depressive symptoms [28]. Suicide has become a more pressing concern as the pandemic evolved [47]. Those with psychiatric disorders experience worsening symptoms and others are predisposed to develop depression, are all associated with increased suicide risk. Media and other news platforms also affect mental health and psychological behavior [35]. Despite the fact that receiving regular updates on COVID-related health information appears to reduce sadness, it is also claimed that social media exposure is linked to depression and mixed anxiety and depression [48, 49, 50]. Unemployment, low social status, a lack of social support, and financial losses are among socioeconomic variables that might contribute to greater incidence of depression. [48, 49, 51, 52]. The added impact of quarantine has led to high occurrence of depression and even self-reported suicidal thoughts [29, 53].
The COVID-19 pandemic has significant negative impacts on healthcare workers’ psychological health, fostering anxiety, depression, and sleep disturbance. Studies conducted during the SARS have reported a prevalence of depression among the front-line HCWs to be 38.5% [54]. The factors in play causing depression are pretty similar to those contributing to anxiety. Increased workload, burnout, inadequate PPE, the risk of contracting the disease, and the challenge of making difficult moral decisions about care priorities during the pandemic have exposed healthcare workers to severe psychological pressures leading to depression. The high infectivity and mortality rates also contribute to depression among healthcare workers around the world. Furthermore, factors such as a high-risk workplace, a lack of clinical experience, young age, and a history of psychological disorders can contribute to depression among healthcare workers. Working on the front lines was found to be an independent risk factor for poor mental health outcomes across all aspects studied, including the prevalence of depression among HCWs. Between doctors and nurses, the latter have been studied to harbor a greater level of depression [55, 56, 57]. This may be partially confounded by the fact that nurses are primarily female but could also be attributed to the fact they may face a greater risk of exposure to COVID-19 patients as they spend more time onwards, provide direct care to patients and are responsible for the collection of sputum for virus detection [55].Emotional exhaustion, depersonalization, being a nurse, 12- or 24-hour shifts or on-call hours, those who live with people who are at risk, and being very concerned about a possible infection of a family member they do not live with are all positively and significantly related to having depression symptoms [58]. Depression is also significantly higher among HCWs who did not know the latest COVID-19-related research/information. Lack of information may precipitate mental health concerns, and prior studies have suggested that updates and knowledge about COVID-19 may have psychosocial impacts, possibly as they represent an active way of coping and dealing with pandemic-related issues [59, 60]. Research also indicates that healthcare workers who take vacation days experience lower levels of depression [26]. Unfortunately, the COVID-19 pandemic crisis witnessed doctors along with other healthcare workers question their choice of profession, in part due to rising infection rates, unavailability of adequate personal protective equipment and other unexpected pandemic related experiences [60].
Long COVID is a term used to describe a condition in COVID-19 patients who have symptoms over an extended period [61]. These patients report prolonged, multisystem involvement and significant disability, which can last for more than six months in 93.2% of patients after the acute phase of illness [62]. Musculoskeletal, cardiovascular, gastrointestinal, pulmonary, and neuropsychiatric symptoms are prevalent in >85% of participants. Fatigue, breathing problems and cognitive dysfunction are among the most debilitating symptoms [62]. Such prolonged physical sequelae are associated with and often are a harbinger of psychological sequelae. Multiple studies conducted during previous pandemics also support the development of psychiatric sequelae in survivors. SARS-CoV-1 survivors exhibited posttraumatic stress disorder (PTSD) with an incidence of up to 55%, depression was observed in 39%, pain disorder in 36.4%, panic disorder in 32.5%, and obsessive–compulsive disorder in 15.6% of SARS-CoV-1 survivors [63]. Long term psychiatric complications such as depressed mood, anxiety and insomnia were also reported in 10-20% of patients following SARS and MERS infections [45]. These complications could result from central nervous system involvement of the virus or perhaps a consequence of fear associated with the infection and isolation itself.
Among the psychological sequelae, the most prominent and popular condition is posttraumatic stress disorder [64]. Surviving a critical illness is known to induce PTS symptoms [65]. Data suggests that as many as 43% of COVID-19 patients suffered posttraumatic stress symptoms [66], ‘not though true prevalence of PTSD’ prevalence. The severity of COVID-19 poses a significant risk factor for PTSD, supported by evidence of higher incidence among ICU patients juxtaposed to non-ICU patients [67, 68]. Even measures of quarantine and isolation, which help to contain the infection from the spread, can have psychological consequences leading to PTSD [2]. Healthcare workers, too, experience significant PTS symptoms. Emotional fatigue, depersonalization, working in a hospital, being highly concerned that someone they live with may become infected, and believing that becoming infected with COVID-19 is very likely are all positively and substantially associated with posttraumatic stress symptoms in the HCW population [56].
Aside from PTSD, COVID-19 infection survivors are more likely to experience depression and anxiety, similar to the acute phase of the disease [64]. The incidence of these disorders is linked to the severity of the disease and the length of hospitalization [69, 70, 71]. The baseline systemic inflammation index (SII) is strongly correlated with anxiety and depression [45, 59]. At follow-up, the prevalence of baseline comorbidities, such as mental disorders and female sex, is also linked to depression and anxiety [71, 72]. Although anxiety and depression symptoms generally go away within 1-3 months after infection, their presence increases the risk of developing PTSD later on [61].
Worldwide epidemiological research is urgently needed to evaluate the amount of anxiety, worry, and helplessness and other mental health concerns related to COVID-19. This would aid in developing tailored mental health initiatives (e.g., those who live in villages). Experiences from past pandemics point out the urgent need for screening for mental health problems, monitoring its trend, referral of cases, and providing suitable interventions has to be routine practice during the pandemic period [73, 74]. The target groups of such screening strategies must include COVID-19 infected patients, their high risk contacts, and healthcare workers, as well as those with past history of mental health illnesses. The following questionnaires may be utilized for routine screening:
COVID-19 stress scale [75]
Perceived Stress Scale modified for COVID-19 (PSS-10-C) [76]
COVID-19 Peritraumatic Distress Index (CPDI) [1]
Anxiety of COVID Scale (CAS) [77]
Fear of COVID-19 Scale (FC-19S) [78]
COVID-19 Pandemic Mental Health Questionnaire [79]
The abovementioned questionnaires were newly developed or modified versions of existing mental health screening questionnaires. It is therefore emphasized that the pre-existing validated survey tools such as GAD-65, PSS-10, PHQ-9 and DASS-21 can be also utilized for routine screening purposes and have been validated for the same.
During the pandemic, marginalized groups such as those with chronic health conditions, mental health issues, and the elderly must continue to get health care. Provisions such as telemedicine and clinics independent from COVID-designated facilities should be guaranteed for optimal healthcare delivery in a continuum.
The mental health and emergency management communities should collaborate to seek, develop, and disseminate evidence-based resources on disaster mental health, mental health triage and referral, unique populations’ requirements, and death communication and bereavement support.
Health care professionals should help patients manage stress and cope (by organizing activities and sticking to routines), introduce patients to social and mental health resources, and encourage them to seek professional mental health help if necessary.
While most psychological health services can be provided in primary care settings, a few patients will require comprehensive mental health assessment and care, while others might benefit from supportive interventions targeted at enhancing wellbeing and coping such as psychoeducation or cognitive behavioral therapy.
Patients with suicidal ideation shall necessitate immediate referral, hospitalization, and therapeutic intervention by a psychiatrist.
Tele-psychiatry services are vital to maintain the continuum of care in the COVID pandemic era. Certain modifications are warranted to enhance the delivery of tele-health services –
At each institution, teams of expert psychologists should be accessible for healthcare professionals to call at any time, and quick treatment and follow-ups should be given.
Peer support and group talks should be promoted as well. HCWs must be provided with stress management training.
During pandemic conditions, vacations from work are required to reduce psychological distress among healthcare professionals, resulting in decreased levels of despair, fear, worry, and stress. Therefore, healthcare professionals are urged to take vacations from work to help them relax, which helps reduce stress.
In regions where infrastructure is lacking, the government and health authorities must work together to provide PPEs and the essential infrastructure for HCWs to safely administer healthcare to their patients.
Health-care systems are required to address the stress on individual providers and overall operations by monitoring reactions and performance indicators, modifying assignments and timetables, moderating expectations, and developing effective mechanisms to deliver psychological support as required.
HCWs should be encouraged to self-monitor their own stress reactions and seek appropriate assistance. Systems must be in place to quickly identify HCWs who require psychological assistance, send them to a professional, and provide access to specialized consultations as well as intervention, if necessary. Setting up of grievance redressal system will be of great use for all the HCWs to raise any issue pertaining to their current work and prompt resolution and solutions can be offered.
The process of testing, quarantine and re-joining work should be streamlined and communicated to everyone working in the establishment.
A rotational basis in the work from highly stressful to low stressful duties can be considered.
Encouragement and establishment of a buddy system with buddies being aware of need for confidentiality and available resources to help the person in distress.
All HCWs should be encouraged to follow a healthy lifestyle such as having a daily routine, pursuing their hobbies and stress management and relaxation techniques such as yoga and breathing exercises.
Isolation and quarantine restrict one’s activities severely, causing worry and concern over not fulfilling one’s professional and familial obligations. While the methods and processes for mental health support and monitoring in quarantine accommodation differ by jurisdiction, there are several critical areas that should be included in national mental health screening, evaluation, and support protocols. These include:
It is necessary to set up telephonic/digital contact between the patients and their family members.
Psychological counseling should be offered in quarantine centers.
Provision of a clear rationale for quarantine and information about protocols
Prior to departure, throughout travel, upon arrival, and during the quarantine period, accurate and timely information is critical.
The provision of a structured day with meaningful activities and opportunities for social connection is critical to preserving mental health. From the start, active participation is required, as well as the availability of a variety of individual and group activities from which to pick. COVID-19-compliant group activities must be delivered, if necessary using virtual methods.
Every person in quarantine should ideally have access to fresh air through windows or a balcony, space to exercise, and healthy and culturally appropriate food options.
People should be actively and assertively engaged in activities, well-being checks, and information.
Effective governance necessitates that all providers involved in assisting persons under quarantine have clear lines of communication, information exchange, and complaint handling.
COVID-19 news, as previously discussed, has a positive linkage with depression, anxiety, and stress levels. It can be caused by erroneous statistics data and rumors that circulate through the internet. To avoid misleading information and public health statistics, the government and health authorities must offer accurate and up-to-date information. In this context, the research found that having access to current and accurate health information is linked to less stress. Up-to-date and exact data on the number of new and improved cases, available treatment methods, and transmission routes must be used to reduce the illness’s psychological and mental health effects. The psychological impact of misinformation/rumor on the mental health of HCWs and the general public might be greatly reduced if a public health information system that is accurate and updated is widely disseminated.
COVID-19 is linked to a range of psychiatric problems in addition to physical health issues. The spread of the novel coronavirus may have an influence on people’s mental health in many communities. Patients with COVID-19 have a significant rate of negative psychological occurrences. Frontline healthcare professionals caring for COVID19 patients have a high rate of stress, anxiety, and depression. As a result, health policymakers should take steps to regulate and prevent mental illnesses among hospital employees. During the COVID-19 epidemic, the mental health of HCWs is a major issue. Psychological interventions that detect and target persons with varying degrees of psychological distress are desperately required.
The authors declare no conflict of interest.
None.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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\n\nPlease complete the publishing proposal form. The completed form should serve as an overview of your future Compacts, Monograph or Edited Book. Once submitted, your publishing proposal will be sent for evaluation, and a notice of acceptance or rejection will be sent within 10 to 30 working days from the date of submission.
\n\n2. SUBMIT YOUR MANUSCRIPT
\n\nAfter approval, you will proceed in submitting your full-length manuscript. 50-130 pages for compacts, 130-500 for Monographs & Edited Books.Your full-length manuscript must follow IntechOpen's Author Guidelines and comply with our publishing rules. Once the manuscript is submitted, but before it is forwarded for peer review, it will be screened for plagiarism.
\n\n3. PEER REVIEW RESULTS
\n\nExternal reviewers will evaluate your manuscript and provide you with their feedback. You may be asked to revise your draft, or parts of your draft, provide additional information and make any other necessary changes according to their comments and suggestions.
\n\n4. ACCEPTANCE AND PRICE QUOTE
\n\nIf the manuscript is formally accepted after peer review you will receive a formal Notice of Acceptance, and a price quote.
\n\nThe Open Access Publishing Fee of your IntechOpen Compacts, Monograph or Edited Book depends on the volume of the publication and includes: project management, editorial and peer review services, technical editing, language copyediting, cover design and book layout, book promotion and ISBN assignment.
\n\nWe will send you your price quote and after it has been accepted (by both the author and the publisher), both parties will sign a Statement of Work binding them to adhere to the agreed upon terms.
\n\nAt this step you will also be asked to accept the Copyright Agreement.
\n\n5. LANGUAGE COPYEDITING, TECHNICAL EDITING AND TYPESET PROOF
\n\nYour manuscript will be sent to Straive, a leader in content solution services, for language copyediting. You will then receive a typeset proof formatted in XML and available online in HTML and PDF to proofread and check for completeness. The first typeset proof of your manuscript is usually available 10 days after its original submission.
\n\nAfter we receive your proof corrections and a final typeset of the manuscript is approved, your manuscript is sent to our in house DTP department for technical formatting and online publication preparation.
\n\nAdditionally, you will be asked to provide a profile picture (face or chest-up portrait photograph) and a short summary of the book which is required for the book cover design.
\n\n6. INVOICE PAYMENT
\n\nThe invoice is generally paid by the author, the author’s institution or funder. The payment can be made by credit card from your Author Panel (one will be assigned to you at the beginning of the project), or via bank transfer as indicated on the invoice. We currently accept the following payment options:
\n\nIntechOpen will help you complete your payment safely and securely, keeping your personal, professional and financial information safe.
\n\n7. ONLINE PUBLICATION, PRINT AND DELIVERY OF THE BOOK
\n\nIntechOpen authors can choose whether to publish their book online only or opt for online and print editions. IntechOpen Compacts, Monographs and Edited Books will be published on www.intechopen.com. If ordered, print copies are delivered by DHL within 12 to 15 working days.
\n\nIf you feel that IntechOpen Compacts, Monographs or Edited Books are the right publishing format for your work, please fill out the publishing proposal form. For any specific queries related to the publishing process, or IntechOpen Compacts, Monographs & Edited Books in general, please contact us at book.department@intechopen.com
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The importance of these compounds in wine is due to their large effect on the organoleptic attributes of wine. Phenolic compounds play a crucial role in the colour as well as mouthfeel properties of wines. UV-visible spectroscopy appears as a suitable technique for the evaluation of phenolic compounds’ properties and content. The ability of the phenolic ring to absorb UV light and the fact that some of the phenolic substances are coloured compounds, i.e. show absorption features in the visible region, make UV-visible spectroscopy a suitable technique to investigate and quantify grape and wine phenolic compounds. A number of analytical techniques are currently used for phenolic quantification. These include both simpler approaches (spectrophotometric determinations) as well as more complex methodologies such liquid chromatography analysis. Moreover, a number of spectroscopy applications have also been recently reported and are becoming popular within the wine industry. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. 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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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