Characteristics of usual metallic implant materials. Values taken from Ref. [6].
\r\n\t
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He received his M.S. degree in Traditional Chinese Medicine (TCM) from Jiangxi University of Traditional Chinese Medicine, and Ph.D. from Nanchang University, China. Furthermore, he joined Department of Biomedical and Pharmaceutical Sciences at University of Rhode Island as a visiting scholar and post doctorate fellow in 2011 and 2014. Dr. Wan’s research interests include phytochemistry and human health benifits, and postharvest biology and technology of citrus fruits. He has served as a Bentham Science Ambassador in 2019-2020 and his research continues to be funded by federal, state, and other agencies. Dr. Wan edited various books, authored over 150 publications, and worked as Editor-in-Chief of World Journal of Biological Chemistry, Associate Editor of International Journal of Agriculture Forestry and Life Sciences, Editor of Frontiers in Nutrition, Evidence-Based Complementary and Alternative Medicine, Journal of Food Quality, Advances in Agriculture, and Journal of Nutrition and Metabolism. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"75036",title:"Mg-Based Composites for Biomedical Applications",doi:"10.5772/intechopen.95079",slug:"mg-based-composites-for-biomedical-applications",body:'It is known that magnesium is a very low-density metal with great importance for structural applications where low weight is desirable. It is approximately 35% lighter than aluminum, 60% lighter than titanium and 78% lighter than steel. Magnesium and its alloys are also potential materials for structural orthopedic implants, since not only Mg’s density (1.738 g/cm3) but also its tensile strength (80–280 MPa) are similar to that of the bone, with advantages as higher fracture toughness [1]. The similar density and smaller elastic modulus mismatch when compared to other biocompatible metals as stainless steel or titanium alloys alleviates stress-shielding effects between bone and the implant material [2].
For being highly biocompatible and biodegradable, Mg is a potential material for producing temporary implants, dispensing a second surgery to remove the implant after the damage tissue is completely healed. Despite their high biocompatibility and no toxic risk during degradation, the corrosion rate of Mg is still too fast, compromising the structural function of the medical device.
Many studies have been conducted over the topic to overcome these issues, and among the potential solutions, the fabrication of composites show promising results. Some reasons for choosing this approach are the possibility to take advantage of bioactive ceramics’ effects as assisting healing or improving the bonding between the tissue and the device, together with the benefits of having a metallic matrix, as good mechanical resistance, better ductility and good energy of impact absorption. Besides, a proper processing technique leads to a more uniform corrosion though the composite, lowering the degradation rate.
This chapter presents an overview having some of the newest studies over Mg-based composites for biological applications and provide a comparison among the processing techniques, effect of reinforcement content in corrosion and mechanical properties.
Medical parts as implants, stents, scaffolds and fixation devices are used when a bone is broken, for supportive structural purposes, bearing the body’s load until the tissue can properly regenerate. These parts must present the following properties:
Be biocompatible and have maximum cell viability to avoid any harm as inflammation, infection, or other adverse reactions;
Not be toxic to the body, and not release toxic elements during degradation,
Have a morphology that improves healing process and osseointegration [3];
Have high enough corrosion resistance to assure mechanical integrity until the healing of the tissue;
Remain stable and integer despite the constant impact of body movement;
Have suitable mechanical properties as high toughness, tenacity, fatigue strength, stiffness comparable elastic modulus to bone to avoid stress shielding effects [4], and sufficient hardness to increase wear resistance and prevent wear debris.
Biomaterials currently used for this application can be polymers, metals, ceramics or composites. Advantage of metals for load-bearing application is the combination of better mechanical strength compared to polymers and better tenacity when compared to ceramics. The most common metals used are titanium alloys, stainless steel and chromium-cobalt alloy, that are bioinert. These metals have high strength to support body load and can keep a good integrity, staying longer in the body due to their high-corrosion resistance [5]. Iron, magnesium, and zinc are also biocompatible metals, with the advantage of being biodegradable in physiological environment. Other metallic biomaterials include shape memory alloys, tantalum and some precious metals [6].
Table 1 summarizes some mechanical properties, density, pros and cons of the usual biocompatible metals [6, 7]. For comparison, the properties of the cortical bones are: a density around 1.8 g/cm3, a modulus of elasticity of the order of 17.0–20.0 GPa in longitudinal direction and 6.0–13.0 GPa in the transversal direction, the tensile strength in the range of 78.8–151.0 MPa in longitudinal direction and 51.0–56.0 MPa in transversal direction, and a fracture toughness in the range of 2–12 MPa.m1/2 [8].
Material | Stainless steel | Cobalt-base alloys | Ti and alloys | Mg and alloys |
---|---|---|---|---|
Density [g.cm−3] | 7.4–8 | 8–8.5 | 4.5 | 1.74–1.81 |
Young’s modulus [GPa] | 200 | 230 | 106 | 45 |
Ultimate tensile strength [MPa] | 540–1000 | 900–1540 | 900 | 80–280 |
Fracture toughness [MPa.m1/2] | ~100 | ~100 | ~80 | ~15 |
Advantages | Cost, availability, processing | Wear resistance, corrosion resistance, fatigue strength | Biocompatibility, corrosion, low modulus, fatigue strength | Biocompatibility, biodegradable, low modulus, low density |
Disadvantages | Long-term behavior, high modulus | High modulus, biocompatibility | Lower wear resistance, low shear strength | High hydrogen evolution during degradation |
Primary uses | Temporary devices (fracture plates, screws, hip, nails) | Dentistry casting, prostheses stems | Femoral heads, long-term permanent devices (nails, pacemakers) | Bone screws, plates, pins, stents, nails |
Characteristics of usual metallic implant materials. Values taken from Ref. [6].
It is possible to notice a considerable mismatch of mechanical properties of these bioinert metals and the cortical bones. When the stress transfer between the implant and the tissue is not homogeneous, stress shielding of the bone may occurs. This takes place by differences between Young’s moduli of the implant and the bone, what may interfere the regeneration process, cause pain, bone atrophy with resorption of the bone surrounding the implant, loosening of the implant and possibly inducing a new fracturing [9]. Almost 10% from overall operations would undergo for revision surgery for replacing a previously implant, and 79% of all revisions were due to implant loosening [10]. The most common cause of implant loosening is the loss of bone mass due to stress shielding [10].
In the cases which the tissue can regenerate, the implant loses its function after the healing process, and additional surgery may be required to remove it. Besides, an implant which stay in the body for a long period of time can induce some complications, i.e. release toxic elements, permanent physical irritation, a chronic inflammatory response, or infection caused by debris and corrosion products [11, 12].
Biodegradable implants seem to be a clever choice for application where the body can eventually regenerate, since this would dispense the need of a secondary surgery to remove it, diminishing risks of inflammation, pain, surgery risks and costs [2, 12]. So, it is of great interest to develop devices which can be gradually degraded and naturally absorbed by the body while assist tissue healing. Yet, it has been challenging to obtain the combination of a controllable degradation rate, good biocompatibility, and suitable mechanical properties.
Among the available biodegradable metals, magnesium is very advantageous since it offers necessary stiffness, toughness and tenacity, high damping capacity, very low density and high biocompatibility. It is the fourth most abundant mineral in the body, being present mostly as surface substituents of the bone’s hydroxyapatite, and in skeletal muscle and soft tissue [13]. Also, Mg is involved in more than 300 essential metabolic reactions, being a cofactor for several enzymes that stabilize RNA and DNA structures. It is necessary to assist in cardiac, muscular, nervous, bone and renal function [14]. Its ion (Mg2+) that is released during degradation is used in the regular metabolism having no critical toxic limits or side effects reported [3, 13]. It has great ability to be absorb and excreted by the body without causing any harm (for example, the corrosion product MgO is totally removed in the urine [11]). During the first half of the 20th century, magnesium was used in surgeries to connect blood vessels and, in some cases, portions of the intestine in animals and in humans. The most advanced clinical applications are biodegradable cardiovascular magnesium stents; however, because it is bioabsorbable, research has been carried out aiming orthopedic applications as non-permanent implants.
The biggest issue that hinders the use of Mg as a biomedical material is its too fast corrosion in body fluid. This characteristic can be related to three main factors: Mg alloys are very reactive, the corrosion products formed in aqueous solutions containing chloride is not protective, and the micro-galvanic interaction between the constituent phases causes the corrosion of the Mg alloys to be significantly greater than that of the Mg of high purity [15].
Besides an early loss their structural function, a fast corrosion releases a big quantity of hydrogen, what may induce some local pH changes, affecting some physiological reactions and leading to an alkaline poisoning effect. Moreover, gas bubbles can accumulate in the implants surrounding surface delay healing and causing necroses due tissue detachments in some cases [3, 16].
Two types of corrosion occur preferentially in Mg and its alloys, galvanic corrosion and localized corrosion (by pitting or filiform). Galvanic corrosion occurs because magnesium generally behaves anodically in contact with other metals, and it is often used as a sacrificial anode. The standard reduction potential of Mg to form Mg2+ is E ° = −2.37 VSHE and one of its main alloy elements is aluminum (Al), which has a reduction potential E ° = −1.66 VSHE in the reaction reduction for Al3+. Galvanic pairs are generally formed when the concentration of the alloying element exceeds the maximum solubility limit. The extent of the galvanic effect depends on several factors, such as the crystalline orientation of the magnesium matrix, the type of secondary phases, particles of impurities, the size of the grain and the medium.
The concentration and distribution of the secondary phases are also important. A fine and continuous distribution of the secondary phases normally increases the corrosion resistance of the metal in various media. Alloy elements and other intermetallic phases formed, with electrochemical potential close to that of magnesium, can increase corrosion resistance, reducing galvanic internal corrosion [16].
Magnesium, when immersed in an aqueous solution, presents a double oxide film, an internal layer of MgO, which is adhered to the metal surface and an external and porous layer of Mg (OH)2 [17]. MgO oxide is a n-type semiconductor, with ΔG formation of −136 kcal/mol at 25°C, has a PB (Pilling Bedworth) ratio of less than 1, and is therefore only partially protective, since the volume of the oxide formed is less than the volume of the reagent metal. The Mg (OH)2 film has a lamellar structure that facilitates basal cleavage, and has a PB ratio of 1.77, which generates internal compressive stress, leading to cracks in the Mg(OH)2 film.
In the case of magnesium, unlike aluminum or steel, the oxide layer is crystalline. There is no epitaxy between the oxide layer and the matrix with the compact hexagonal mesh (HCP), leading to a high compressive stress of the layer [18]. One of the ways to reduce discontinuity and have less disorder between the oxide layer and the metal surface is to introduce a large fraction of grain outlines per unit area [19].
Cathodic reactions in neutral or alkaline aqueous media are oxygen reduction to hydrogen evolution, expressed in Eq. 1:
In order to have a complete understanding of the Mg corrosion mechanism, it is necessary to consider a phenomenon called Negative Difference Effect (NDE). This phenomenon is experimentally characterized by an unexpected increase in the hydrogen evolution reaction when the anodic overvoltage is increased. NDE is the main cathodic reaction in the corrosion of Mg and occurs spontaneously in Mg under open circuit conditions. When Mg and its alloys are anodically polarized, hydrogen evolution also occurs, which is normally restricted to the cathodic branch. Anodic polarization causes higher rates of dissolution, and higher rates of NDE are also observed [20]. To explain the corrosion of magnesium and more particularly the NDE phenomenon, several mechanisms have been proposed including the formation of magnesium hydrides, metastable monovalent ions, hydroxides and magnesium oxides [17].
The corrosion rate of magnesium-based biomaterials, when implanted in the human body, depends on the composition of the material and the environmental conditions in which the implant is found, such as temperature, pH and concentration of other ions. A study [21] indicates that, the effects of Mg corrosion products on cellular activity is concentration-dependent, on bone marrow derived stem cells and on osteoclastogenesis
Representation of cellular response in the presence of a corroding Mg biomaterial
The corrosion rate of Mg can be reduced by increasing the intensity of the basal planes parallel to the surface [22]. Other strategies to diminishing the degradation rate are grain refinement, alloying, surface modification, conversion coating, and incorporation of other materials to create composites. It is worth noting that, in the case of composites, some of these strategies can be combined. For example, a processing technique that enables a refined microstructure can be selected. Grain refinement has a recognized importance for improving mechanical resistance and also for diminishing the corrosion rate with time due to the development of a more homogeneous protective layer on composites surface [23, 24, 25]. Also, the matrix can either be pure Mg or an alloy. Although many Mg-alloys are been developed for improved corrosion resistance and superior mechanical strength [4, 11, 26], care must be taken to choose a composition free of any toxic elements. The insertion of a biocompatible second phase in a magnesium matrix could bring a better mechanical resistance and less pitch corrosion. Furthermore, some bioactive materials that could promote a better bonding between implant and the tissue can be incorporated and accelerate body’s healing response in some cases. Also, the addition of a hard phase, combined with mechanical processing, enables a better grain refinement and strengthening. Thus, magnesium matrix composites are potential candidates for structural orthopedic implants.
There are three determinant factors for the biocompatibility, mechanical strength and corrosion behavior of the Mg-composite: grain size, second phase distribution, and materials composition. The first two can be controlled by fabrication and processing technique. The deformation route directly influences the texture of the material, that has a great influence on magnesium corrosion. Also, the reinforcement selection (nature, fraction and morphology) is of great importance for dictating the composite’s properties, and it will determine if the device is toxic, inert, or bioactive.
Elements commonly used for Mg alloying are Al, Ca, Cu, Fe, Li, Mn, Ni, Sr., Y, Zn, Zr, and rare earth elements.
Aluminum is the most usual alloying element for Mg. It can enhance the alloy strength by both solid solution and precipitation of intermetallics. Another advantage is the low density of this metal (2.7 g/cm3). Although alloys as AZ91 and ZK60 have been used for biomedical applications, this element should be avoided due to the risk factor of Alzheimer’s disease, muscle damage, decrease of activities of osteoclasts, altered functions of the blood–brain-barrier, and increase estrogen-related gene expression in human breast cancer cells when cultured in a laboratory setting [11, 27].
Manganese can improve Mg corrosion resistance by reducing the harmful effects of impurities [27]. Mn is also an essential biological trace mineral that acts in many cellular systems, especially as cofactors for many metalloenzymes as oxidases and dehydrogenases, DNA and RNA polymerases, kinases, decarboxylases and sugar transferases, although excessive amounts could induce neurological disorder [27].
Zinc is another common alloying element in Mg. It is nontoxic, biocompatible and biodegradable, and plays a significant role in human body. Zn is present in metabolic activities, as co-factor for some enzymes and it is essential for immune system. It is readily absorbable by biological functions within the cell [27]. The addition of until 4% wt. Zn increase ultimate tensile strength and elongation of as-cast Mg-Zn alloys.
Calcium is the most abundant mineral in the human body, important or bone function, vascular and heart physiology. Adding in small amounts, it can contribute for mechanical strengthening by solid solution, precipitation and grain boundary pinning. Mg2Ca is brittle and the addition of >1%Ca deteriorate ductility and mechanical properties. Despite Ca positively influence the cell viability and proliferation rate, the effect of Ca in corrosion resistance is deleterious because it accelerates degradation due to galvanic corrosion.
Kirkland et al. [28] perform a systematic study comparing the degradation of a series of Mg-Ca-Zn alloys. Figure 2 present average values of the maximum compression strength of their Mg-xCa and Mg-xZn alloys after 1 and 3 weeks of immersion in SBF, in relation to the alloying content. The blue lines indicate the compressive strength of pure Mg, after 3 weeks in SBF (lower line) or without immersion (upper line).
Effect of alloying content in the compression strength of Mg-xCa and Mg-xZn alloys after 1 and 3 weeks immersed in SBF. Based on Ref. [
An issue related to the most common alloys is concerned about the biocompatibility and toxicity of alloying elements [29]. The alloying elements might me selected to not only improve corrosion and mechanical properties of the alloy, but also to improve the body response. But mostly, special care must be taken to avoid elements that may release corrosion products that can be toxic to the body. A summary of the influence of metal ions on the variety of processes involved in bone regeneration is depicted in Figure 3 [30].
Summary of the influence of metal ions on processes involved in bone regeneration [
The reinforcement selection (nature, fraction and morphology) is of great importance for dictating the composite’s properties. Primarily, it should be highly compatible and safely absorbable by the body without any harm to the organism. It should also achieve a good interfacial bonding with the matrix, since a discontinuous matrix can lead to stress concentration, facilitating crack evolution and increasing localized corrosion.
The most common materials used to reinforce Mg-based composites aiming biomedical applications are silica-based or phosphate-based ceramics [4]. Bioactive glasses (BG) is an important silica-based material that can make a strong bonding to the bone. It is intrinsically brittle. They display significant bioactivity, improves hemocompatibility, and the ionic dissolution products of bioactive glasses stimulate osteoblast proliferation [31]. They are used in dental implants and for artificial bone. Some papers have reported that addition of BG to Mg matrix can improve the biocompatibility of pure Mg [32]. Yet, calcium phosphates such as hydroxyapatite (HA) and tricalcium phosphate (TCP) are the most popular choice to fabricate Mg-matrix composites aiming biomedical application. Β-TCP has a great importance for being bioresorbable, bioactive and osteoconductive material. It is used for bone tissue regeneration. HA can form strong chemical bonds with the osseous tissue and it is one of the main components affecting the mechanical strength to bone, providing it stiffness [1]. It is commonly used as orthopaedical implants, dental implants and coating metallic implants, tissue engineering scaffolds. Yet, bone grafts made exclusively of HA present a low fracture toughness (0.7 MPa.m1/2, [16]).
The opportunity to match the superior compressive strength and biological performance of bioactive ceramics, with the toughness and resilience of Mg and production of biodegradable and bioactive composites is of great research interest.
There are many researches over the fabrication of a magnesium-based composite with biocompatible ceramic reinforcements dispersed into the metallic matrix. Many review papers about Mg-based composites for biomedical applications have also been published, covering since fabrication methods, mechanical properties improvement, corrosion behavior and also biocompatibility
The fabrication methods used to produce Mg-based composites for biomedical purposes are listed below. The processes techniques can be divided in either liquid state (casting) or solid-state processing route (powder metallurgy). Routes that involves casting promotes the insertion of the hard phase in a liquid bath of the matrix-metal, while solid-state routes achieve dense consolidated composites without melting the materials. In this case, the particles consolidation should be induced by high-temperature diffusion and/or plastic deformation. To ensure a full densification of the composite, great majority of the solid-state techniques takes place at high temperatures, but below materials’ melting point. All processing techniques should enable an efficient matrix-reinforcement bonding and a homogeneous dispersion of the hard phase, since agglomeration and internal cracks are deleterious to corrosion and mechanical properties [36, 37].
Many metallic composites are synthesized by casting techniques. They can be fabricated by pumping the melted metal at high pressure into a die which can contain the reinforcement dispersed or as a preform. Alternatively, the hard phase can be added to the matrix by stirring it into the melted metallic bath.
Khanra et al. used stir casting to fabricated Mg-HA and ZM61-HA composites having different amounts of HA (0, 5, 10 and 15 wt%), then performed hot extrusion at 320°C on the billets [38, 39]. The addition of HA induced grain refinement, an increase in compressive strength but a decrease in tensile strength. Since the alloy have superior strength than the pure metal, (ultimate tensile strength – UTS of the Mg-0 HA is 187.9 while UTS of ZM61-0HA is 301 MPa) the ZM61-HA composites presented higher mechanical properties than Mg-HA ones (UTS of Mg-15HA is 136.7 MPa, against 225.5 MPa from the ZM61-15HA).
Melting and hot extrusion were also applied to synthesize Mg-Zn-Zr matrix composites with 0, 0.5, 1 and 1.5 wt% HA as reinforcement [40, 41]. Common results were the improved of mechanical properties with HA addition, and reduction in corrosion rate for composites having 1 wt% HA.
Kumar et al. [42] synthesized HA-MgO composites using 0, 0.25, 0.5, 1 and 2% MgO by melting infiltration. The mass loss, after 21 days in SBF, of pure HA is 0.43 ± 0.08 mg, which is significantly higher than the HA-1.0 MgO composite (0.21 ± 0.03 mg). As increasing the amounts of MgO, a remarkable increase in the mechanical properties of the composite was achieved.
Powder metallurgy (PM) techniques involves a methodology composed of powders mixture, compaction and sintering of the materials, usually in an inert atmosphere. The mixture is commonly performed by ball milling especially when a better interaction between matrix and reinforcement is expected. Uniaxial pressing at room temperature is generally applied to create the green compacts before the sintering steps. Other processes as isostatic pressure, hot pressing [43, 44] and double-step sintering [45] have also been used to assure a better densification of Mg-HA composites. Conventional sintering strongly relies on diffusional processes, that are favored by longer times and higher temperatures, and this can lead to a significant grain growth during the process.
Setyadi et al. [46] fabricated Mg-CA composites having 5, 10, 15 wt% of carbonate apatite (CA) by powder metallurgy. The higher milling times and higher content of CA lead to an increase in hardness of until ~20% when compared to pure magnesium (hardness whet from 37.3 HV in pure consolidated Mg to 44.8 HV for 7 h of milling and 15% CA). The optimal composition was obtained for the composite with 10 wt% of CA. Salleh et al. [47] produce Mg6.5Zn matrix composites with 0 and 10 wt% of HA by mechanical milling and PM. The progressive effect of HA followed by the Zn addition, decelerate the degradation rate of Mg. The composite exhibited the highest corrosion resistance in Hank’s Balanced Salt solution (HBSS). The compressive strength for the alloy and the composite, after 7 days of immersion, are considerably higher compared to the cortical bone.
Double step sintering PM (450°-550°) was applied by Jaiswal et al. [45] to fabricate Mg-3Zn matrix composite reinforced with 0, 2, 5 and 10 wt% of HA. It was reported that the addition of 5 wt% HA is found effective in reducing the corrosion rate by 42% in SBF. Mg-Zn with 5% HA showed improvement in the compressive yield strength of biodegradable magnesium alloy by 23%.
Ball milling and hot extrusion at 270°C were used by Stüpp et al. [43] to produce ZK60-HA composite with 0, 10, 20 wt% HA. A slight improvement in the corrosion resistance was observed for the composites in DMEM + FBS. Samples showed a slight increase in the compressive yield strength with the addition of HA.
Spark plasma sintering (SPS) apply high-pressure to enable a better densification of the sample using considerably less time and lower temperatures than in conventional sintering, and thus achieves finer grain sizes. Sunil et al. [48] used ball milling and SPS at 450° to produced Mg-HA composites having 0, 8, 10 and 15 wt% of HA. Fracture toughness and Young’s modulus decreased with the increase of HA content, but were improved in relation to the as cast Mg. The Mg-10%HA composite present the better corrosion resistance among the samples.
Microwave-assisted processing is another sintering technique which a uniform heat is induced with an accurate energy balance by electromagnetic waves in a high-frequency electric field. It promotes a rapid sintering of the Mg to a sample which density is near the theoretical value, using relatively low energy consumption. This approach was used by Wan et al. [32] to produce Mg-BG composites with 5, 10 and 15% of BG. The materials were ball milled and sintered in a microwave furnace at 500°C. It was reported that the addition of bioactive glass can considerable reduce hydrogen evolution, avoid significant pH change and improve biocompatibility when compared to pure Mg. The best mechanical and biological behavior was presented by the Mg-10%BG composite. The improvements in compressive strength and modulus are 34.0 and 18.7%, respectively, compared to pure Mg. Xiong et al. [49] also used the same procedure to prepare Mg-HA composites having 5, 10 and 15 wt% of HA, and observed that mechanical properties and corrosion resistance of Mg-HA composites were better than the ones of pure Mg. The HA was well dispersed in all samples, but the optimum composition was found to be 10% of HA. This reinforcement addition not only presented the lower corrosion current density (1x 10−4 A/cm2) but also increased the compressive strength and modulus by about 67.5 and 42.8%, respectively.
In the available review papers, it is possible to notice that all methods described to produce the biodegradable composites involve at least one steep of high temperature processing. Yet, it was recently reported that different bioactive composites were fabricated by high-pressure torsion (HPT) at room temperature, combining Mg and alloys with bioactive reinforcement as hydroxyapatite or bioactive glass [50].
Severe plastic deformation (SPD) are processes techniques where high hydrostatic pressures are applied while the materials goes through an intensive shear, but without significant change in sample’s dimensions. This enables multiple consecutive process steps and then accumulation a great amount of deformation. As a result, the processed materials go through extensive grain refinement, homogenization of second phases’ distribution and alteration of the texture, which can contribute to the reduction of corrosion [24, 25, 37, 51]. Yet, it is observed that corrosion analysis of magnesium alloys with ultrafine grains is particularly complex, as it may involve factors such as texture, formation of unstable passive film and of heterogeneous structures resulted from dynamic recrystallization.
Equal channel angular pressing (ECAP) and high-pressure torsion (HPT) are well-known SPD techniques that can not only process bulk samples but also consolidate metal particles and produce metal-matrix composites with a refined microstructure. The mechanism of powder consolidation SPD processes differs from conventional sintering routes because the bonding between particles is caused by deformation of the particles, rather than the atomic diffusion. Thus, shorter times and lower temperatures are needed to consolidate the particles into a fully dense sample.
Silva et al. [25] reported the beneficial effects in SPD Mg in compared to as cast or hot rolled samples. As is showed in Table 2, the SPD processed samples present smaller grain sizes, thus better mechanical properties and better corrosion properties. Besides the formation of a protective film composed of corrosion products - that reduced the loss of mass after long intervals immersion – Mg biocompatibility was not affected by this treatment.
Processing | Grain size (μm) | Yield stress (MPa) | Corrosion potential (V) | Corrosion current (A/cm2) | Corrosion rate* (g/cm2/h) |
---|---|---|---|---|---|
As cast | 480 | 34 | −1.48 | 7 | 54 |
Hot-rolled | 16 | 110 | −1.61 | 350 | — |
Hot-rolled + ECAP | 3.2 | 140 | −1.51 | 40 | 25 |
HPT | 0.56 | 80 | −1.49 | 8 | 19 |
Properties of pure Mg processed by different techniques [25].
Corrosion rate calculated from the H2 evolution after 48 h of exposure in 3.5% NaCl solution.
It is difficult to standardize and control the texture of the alloys during SPD processing. However, it is known that HPT processing leads to a preferential orientation of the grains, of the magnesium alloy, in (0002) or basal plane. Since the basal plane is more stable than other non-oriented planes or grains, thus the increase in oriented grains (0002) increases corrosion resistance [18].
High-pressure torsion was also used to fabricate Mg-based bioactive composites, but at room temperature [50, 52]. The matrix where composed by commercially pure Mg or the AZ91 alloy, and the selected reinforcement were bioactive glass or hydroxyapatite. SEM images of the materials are depicted in Figure 4.
Particles used as starting materials to produce Mg-based composites at room temperature using high-pressure torsion [
The AZ91–5%BG and Mg-5%BG composites [50] exhibited small impedance arcs and fast corrosion in HBSS due to the presence of cracks developed inside BG particles during processing. Adding the bioactive ceramic improved hardness of all composites, and this gain was more expressive for the ones with CP-Mg matrix (77% higher than the monolithic HPT-processed Mg). It was reported that a good dispersion of HA and an efficient consolidation of the matrix were achieved for the Mg-5HA (Figure 5). The tensile strength and the hardness of this composite was improved in relation to the pure metal. As Mg is soft and the particles are relatively big, it can easily deform around the ceramic particles, either HA or BG. On the other hand, AZ91 has a better strength than the pure metal (Figure 6a), so it is harder to consolidate. Besides, bioactive glass particles are sharp and fragile, so they can fracture during processing and induce localized corrosion in discontinuities surrounding.
(a) SEM backscattered electron image of the mid-radius area of the Mg-HA composite, and (b) it respective stress vs. strain curve [
(a) Microhardness distribution along the disc diameter in the different Mg composites, and (b) electrochemical impedance spectroscopy tests in Hank’s solution for the different composites [
This corroborates with the poor corrosion resistance of the BG reinforced composites, especially the AZ91–5BG, as shown in Figure 6b. The size and nature of the reinforcement are indeed determinant for slower corrosion rate due to the tendency to develop cracks during processing. This fact was also observed in Mg-quasicrystals composites [53] which fractured along the coarse quasicrystalline particles and on the matrix-reinforcement interface after tensile test.
In the Mg-5%HA composite [52] the addition of HA enhanced the corrosion resistance after 10 hours of immersion in HBSS, the impedance increases and the mass loss rate decreases in the composite. The composite exhibited enhanced hardness and ultimate tensile strength in comparison to pure Mg process by HPT. The corrosion evolution behavior of the Mg-5%HA reported by Lopes et al. [52] seems to be slower than for the Mg-5%BG reported by Castro et al. [50] due to the smaller size and morphology of HA particles, what facilitates a better continuity of the matrix and less stress concentration on the Mg-5%HA composite. In relation to pure Mg, immersion tests of the Mg-5%HA composite leads to a resulting superficial layer that appears to be thicker and the corrosion seems to be more generalized, what contributes for the diminishing of the corrosion rate after longer immersion times (Figure 7).
(a) Mg and (b) Mg-HA surfaces after immersion in Hank’s solution. (reproduced with permission [
In another recent work, a combined method of cyclic extrusion compression (CEC), equal channel angular pressing (ECAP) and conventional extrusion were employed to fabricate Mg-HA composites with 2, 5 and 10 wt% HA at 400°C [54]. Among all samples, the Mg-5HA composite presented the finest grains and exhibited the highest hardness and strength. Hydrogen evolution tests and potentiodynamic polarization tests showed that among all samples, this composite has the lowest values of evolved hydrogen, weight loss and corrosion current density. Its corrosion resistance is also better than in cast and extruded pure Mg. The CECAP-FE process proved to be able to achieve a good and homogenous reinforcement dispersion until 5 wt% of HA. Adding more reinforcement deteriorate mechanical properties and corrosion resistance, what is directedly related to the development of the inhomogeneous microstructure and HA agglomerations.
The thin surface layer of magnesium oxides and hydroxides formed when these materials are exposed to neutral or alkaline solutions have time-dependent stability and, in many studies conducted on magnesium alloys with ultra-fine grains, the results for the evolution of this stability over time are inconsistent [55]. For example, in the research works of Minárik et. al [56] and Zhang et. al [57], there was an increase in the stability of Mg oxides and hydroxides over time, while Song et. al. [58] observed a decrease in this stability. There are also studies in which growth and decreased stability were reported during the analysis period [59]. This factor is relevant, and as the results of polarization tests or hydrogen evolution - which involve immersion - may be affected by the protective oxide instability [55]. The contradictory conclusions of corrosion studies in pure magnesium and magnesium alloys may be due to these microstructural heterogeneities resulting from the recrystallization dynamics [55, 58, 59, 60, 61].
Magnesium-based composites are recognized as promising materials for biomedical applications due to the combination of good mechanical properties, low density, and possibility to achieve a slower corrosion rate and enhance healing by the presence of bioactive materials.
Several studies conducted by many research groups all around the word leads to the conclusion that a more controllable corrosion rate relies on a homogeneous second phase distribution, and refined matrix absent of cracks or stress concentrators.
The use of fabrication methods based on the plastic deformation of metallic particles requires less time and lower temperatures to produce whole dense materials in comparison to techniques primarily relied on diffusional processes. These processes enable the achievement of smaller grain sizes and thus better mechanical properties and slower corrosion rates. Besides, the use of SPD for processing Mg-based materials does not compromise biocompatibility.
The possibility to create composites without using high-temperature steeps is interesting not only due to the operational and energetical advantages (it is less energy and time consuming, generates no fumes, and avoid grain growth), but also due to the potential of creating controlled drug delivery system with medicines that are temperature sensitive and could deteriorate during conventional processing.
It appears that combining materials and techniques could be more efficient than a singular approach to overcome the current issues to produce an ideal biodegradable implant. Yet finding the best materials combination, adjusting their proportion, and selecting the most suitable fabrication route with optimized parameters is still challenging.
The authors are grateful for the publisher support.
The authors declare no conflict of interest.
Air pollution is defined as the presence of solid, liquid or gaseous components in the atmosphere that can cause risk and troubles for living beings or goods in general. Air pollution is one of the major environmental problem in modern human history [1]. Environmental pollution can be produced by natural or human actions. Natural sources include forest fires, volcanic emissions, dust, sand, vegetation (as pollen) and wildlife (as methane). The main human sources of air pollution are industry, power generation, transportation, deforestation and cattle raising [2].
The current exponential growth in world population heightens the importance of public health issues related to air quality [3, 4]. In developing countries, decision makers must cope with the environmental demands of expanding and overpopulated urban centers. Short term air quality forecasts and long term mitigation strategies for these centers are usually based on specialized assessments of particulate matter dynamics [5, 6]. The Aburrá Valley houses the city of Medellín and neighboring municipalities. It is the second most populous urban agglomeration in Colombia, and the third densest in the world. The valley traces the course of the Medellín River along 60 km of a deep mountain canyon that ranges in width between 3 and 10 km, and with a height difference of up to 1800 m. Air quality conditions deteriorate severely within the valley twice a year around the time of the arrival of the Intertropical Convergence Zone (March–April, and with lower intensity in October–November), when the atmospheric inversion layer persists throughout the day below the rim of the canyon, thus trapping all of the urban atmospheric contaminants within the lower atmosphere [7]. During these periods, the concentrations of particulate matter below 10
Perspective of the air quality in the city of Medellín. (August 26, 2016,
Due to the large stress on human health induced by this air pollution, efforts have been made to monitor, reduce, and prevent episodes in which concentrations of pollutants reach hazard levels. Before measures for reducing air pollution can be implemented it is important to know the actual concentration levels and how these evolve in time over the area of interest. This could be done using a Chemical Transport Model (CTM) to simulate concentrations of trace gasses and particulate matter [8, 9]. In the last 20 years, CTMs have seen a huge growth and development; in consequence a diversity of models exists, differing in their complexity, size of the region of study, and methods used for their development. CTMs can be broken down in four categories according to their dynamic behavior: i) Gaussian, ii) statistic, iii) Lagrangian and iv) Eulerian [8]. Eulerian models are the most widely used and reported for monitoring and predicting the pollution behavior and define the air quality in bigger areas [9]. So, these are frequently used in areas with sizes like countries or continents and have been less used in areas like cities.
Data assimilation (DA) is a mathematical process that provides integration between measured values (observations) and a dynamic model, to improve the operation of the model. With DA, the output value provided by the model has a smaller error than the output value provided by the model without observations. DA has two key objectives: to improve the operation in predictions of model states; and estimate unknown parameters of the model [10]. DA has been tested in different science fields such as oceanography, climatology, CTMs, and reservoirs characterization [11]. DA allows integrating models and observations out different scales of size and temporal sampling [12]. When two sources of information are combined, DA assumes that both the model and the measurements are subject to errors. These errors are impossible to know with accuracy and need to be specified in statistical and probabilistic terms. DA is not only looking to reduce the model error in space or time with observations; its mission is to digest the observation based on the laws given by the model and to determine the dynamic evolution of the model state that represents better measurements [13].
Large-scale model uncertainty, especially in CTM, is a very complicated issue. Increasing the accuracy of initial conditions, such as accurate land cover representations or updated emissions inventories, or using observations and DA, may reduce uncertainty. Data assimilation offers an alternative that is dynamically driven to reduce the lack of knowledge about the behavior of air pollution. The addition of surface, satellite, in situ, and laser-based remote sensing data to a model will enhance the understanding of proper scenario simulation and online decision-making. A bounty promise lies in the incorporation of the DA, not only for its contribution to the reduction of uncertainty, but also for opening the door to air quality forecasting in atmospheric pollution modeling. CTM forecasting presents us with interesting and complex challenges associated with the uncertainty of weather forecasting, the lack of precise inventory of emissions, and the scarcity and sparsity of monitoring networks for air quality. Such challenges require creative solutions; these challenges are opportunities for knowledge advancement. Due to the scarcity of data and high uncertainty in the model inputs, a mathematical, analytical, and computational effort is needed to push the frontiers of knowledge in the field.
Public air quality monitoring networks often consist of fixed measuring stations equipped with expensive sensors and maintained under rigorous operational and calibration regimes in order to provide high quality data. The high costs associated with establishing and maintaining such stations means that not all cities in developing countries can afford monitoring networks of sufficient spatial coverage [14]. Even in large cities in developed countries, the official air quality monitoring networks do not always provide information at the spatial and temporal resolution required to assess the impact of pollution sources on health [15], as the cost of the equipment makes the necessary density prohibitive. In the metropolitan region of Medellín (Colombia) and its con-urban municipalities for example, there are 21 main PM2.5 monitoring stations, at an average density of 8.25 km2 over the entire area of the 10 municipalities. This has motivated the expansion and improvement of low-cost systems and programs to measure PM [16]. The limited number of studies that have evaluated newer generations of low-cost PM2.5 sensors have shown that the most widely used low-cost sensors attain high accuracy when compared to standard monitoring stations (R2 value ranging from 0.93 to 0.95) [17]. The data provided by these sensors can complement those generated by conventional systems, increasing the data resolution and allowing studies of exposure at the human level [15, 18]. By data assimilation, the incorporation of air pollution data into CTM increases the ability to grasp local and regional patterns and fill spatial coverage gaps. Additionally, the combination of different sources of information and knowledge (data and model) increases the robustness and reliability of low-cost observations [12, 19].
The Ensemble-Based DA is a family of methods that uses an ensemble to model the statistics of the first guess (background). In each assimilation step, a forecast from the previous model simulation is used as a first guess, using the available observation this forecast is modified in better agreement with these observations. Due to it is easily implemented, it is relatively low in computational costs (compared with other DA techniques), and has a very general statistical formulation it is one of the most widely used approaches for tackling real-time forecasting problems [20].
The Ensemble Kalman filter (EnKF) is the main Ensemble-based DA method [21]. Based on the Kalman Filter (KF) [22], EnKF is an alternative for nonlinear, high-dimensional systems. EnKF essentially is a Monte Carlo Ensemble-based method, based on the representation of the probability density of the state by an ensemble of
Representation of Kalman filter (upper) and ensemble Kalman filter (lower).
Understanding local and regional atmospheric particulate matter transport patterns becomes a top priority for urban valleys in the northern Andes. This work will help establish accurate air quality forecasting systems for the Aburr’a Valley (and other similar areas) and improve decision-making. Chemical Transport Models (CTM) are valuable resources for understanding atmospheric pollutants’ dynamics and have thus been widely used in air quality monitoring [8, 9].
Here we use simulations of the LOTOS-EUROS (LE) chemistry transport model (CTM) to investigate the atmospheric contaminant dynamics in the Aburr’a valley, which spans ten municipalities, including Medellín city. The
A data assimilation method for the LOTOS-EUROS chemical transport model has been introduced to boost the PM10 and PM2.5 forecasts. The system uses an Ensemble Kalman filter with covariance localization, which is based on the specification of emissions uncertainties. The data was gathered from a surface network for the months of March and April 2016, during one of the region’s worst air quality crises in recent memory. The SIATA is spread around the five most populous municipalities in the Aburrá Valley, with the bulk of the measuring stations in Medellín. Figure 3 represents the distribution of observation sites.
SIATA sensor network for PM10 and PM2.5. The stars represent observation points for validation and the circles represent observations points for assimilation. Taken from [
Measurements for one station for each species (represented with a star in Figure 3) were used for validation, taking two stations with a considerable distance between them to obtain a acceptable spatial representation.
In a first series of experiments, the spatial length scale of the covariance localization and the temporal length scale of the stochastic model for the emission uncertainty were calibrated to optimize the assimilation system. The calibrated system was then used in a series of assimilation experiments. The summarized experimental setup is presented in the Figure 4.
Graphic representation of the experimental setup. Taken from [
Simulations were conducted with the LE model, adopting a nested domain configuration as depicted in Figure 5 and detailed in Table 1. The data sets used in the model are summarized in Table 2.
Four nested domains for metropolitan area of Aburrá Valley assesment. Taken from [
Domain | Longitude | Latitude | Cell size |
---|---|---|---|
84 | 8.5 | 0.27 | |
80.5 | 2 | 0.09 | |
77.2 | 5.2 | 0.03 | |
76 | 5.7 | 0.01 |
Nested domain specifications.
Period | 31-March-2016 to 25-April-2016 |
---|---|
ECMWF; Temp.res: 3 h; spat.res: | |
LOTOS-EUROS (D3). Temp.res: 1 h. | |
Spat.Res: | |
EDGAR v4.2. Spat.res:10 km × 10 km | |
MEGAN Spat.res:10 km × 10 km | |
MACC/CAMS GFAS Spat.res:10 km × 10 km | |
GLC2000. Spat.res:1 km × 1 km | |
GMTED2010. Spat.res: 0.002 |
Data set used in the D4 domain.
Estimated PM10 emissions and EDGAR nominal emissions are shown in Figure 6. The emissions hot-spots occur in rural zones with limited human activity in the EDGAR database. The estimated emissions attempt to remedy this behavior by projecting the most of the pollution into the metropolitan region of the valley (Figure 6).
Comparison between EDGAR PM10 and estimated PM10 emissions. Taken from [
The assimilated PM10 concentration match closely those measurements at the Universidad San Buenaventura (center of the valley) from April at 19:00 UTC-5 through April 25 at 11:00 UTC-5 (see Figure 7). The peak around 18:00 (and usually all day up to that hour) may be unreliable, which may be because of EDGAR’s temporal emissions factors. Additionally, concentrations can be increased by the meteorological fields. Note that the daily cycle for the assimilated model remains closer to the observations than the model without assimilation.
PM10 validation for the second DA iteration. Estimated emissions were used as nominal emissions, the estimated observation error covariance is used in the assimilation step. Red points are observations, solid black line is the free run model and the solid blue line is the analysis step for the assimilated model. The diurnal cycles were obtained from 13 samples for each hour. The shadows and the bars represent the standard deviation of the 13 samples. The time axis corresponds with the local time zone UTC-5. Taken from [
Figure 8 shows a similar comparison for the PM2.5 station. The model in a free run tends to over estimate the PM2.5 concentrations (see peaks in 15 April at 23:00 UTC-5, 24 April at 22:00 and 25 April at 23:00 UTC-5). The results of the assimilation process offer a better average estimation. The daily cycle of PM2.5 within the Aburrá valley is related to the industrial and mobile sources emissions profile and the meteorological conditions inside the valley.
PM2.5 validation for the second DA iteration. Estimated emissions were used as nominal emissions, the estimated observation error covariance was used in the assimilation step. Red points are observations, solid black line the free run model and solid blue line the analysis step for the assimilated model. The diurnal cycles were obtained from 13 samples for each hour. The shadows and the bars represent the standard deviation of the 13 samples. The time axis corresponds with the local time zone UTC-5. Taken from [
Poor air quality is a current environmental problem in several Colombian cities. To be prepared for air quality degradation requires accurate and reliable data for decision-making in South America. This study shows that the LOTOS-EUROS model can function in areas with more complex topography, such as the Abura Valley, and encourages the development of fine-tuned weather forecasting systems to support the target. The use of regional, ground-based pollutant data from the SIATA sensor network, in the assimilation of the LOTOS-EUROS model, enhanced the PM10 and
Public air quality monitoring networks frequently consist of fixed measuring stations equipped with expensive sensors and maintained under strict operational and calibration regimes. Because of the high costs of setting up and maintaining such stations, not all cities in developing countries can afford monitoring networks with sufficient spatial coverage [14]. Even in developed cities, official air quality monitoring networks do not always provide information at the spatial and temporal resolution required to assess the impact of pollution sources on health, [15], due to the equipment’s high cost. This has prompted the development and improvement of low-cost PM measurement systems and programs. According to [17], a small number of studies evaluating newer generations of low-cost PM2.5 sensors have found that the most widely used low-cost sensors achieve high accuracy when compared to standard monitoring stations (R2 values ranging from 0.93 to 0.95). The data collected by these sensors can be used to supplement that collected by traditional systems, increasing data resolution and allowing studies of human exposure [15, 18].
Using techniques like data fusion or data assimilation to integrate observations from dense networks of low-cost sensors into mathematical models allows for a spatially continuous representation of concentration fields with significantly reduced bias citeLahoz2014. By spatially interpolating between monitoring locations and constraining the model with observations, these techniques add value to the sensor observations while also adding value to the model [17, 18, 25]. Both sources of information can thus be combined in a mathematically objective manner to reduce the uncertainty inherent in both sources [12]. Although data assimilation is a more complex family of methods than data fusion or interpolation techniques, it is the most versatile and robust of these approaches. The goal of evaluating the data from the low-cost sensor network as an alternative to monitoring PM2.5 concentrations in developing countries is to see if it is viable.
The SIATA project operates the official high-end air quality monitoring network (henceforth
The low-cost network was created with the aim of engaging the community in issues surrounding air quality, and as an extension of the official network. The low-cost network consists of 255 real-time PM2.5 (Figure 9, panel b).The measuring equipment was developed by SIATA based on the well-known low-cost Shinyei PPD42NS, NOVA SDS011, and Bjhike HK-A5 sensors [27]. Each low-cost sensor is calibrated individually against BAM-1020 measurements [27]. The calibration process showed the measurements of 91% of the low-cost sensors with correlation values above 0.6 against the official measurements, and 67% with values above 0.8. The median of the root mean square error showed a value of 6.2
Spatial distribution of the hyper-dense low-cost network citizen scientist and official monitoring air-quality network for PM2.5. The gray raster represent the LOTOS-EUROS model grid. Taken from [
An anthropogenic urban emissions inventory for 2016 specific to Medellín and the other nine municipalities of the Aburrá Valley was used for the simulations on the D4 domain. The construction of the inventory followed a bottom-up methodology, combining activity data (traffic intensities, industrial production) with emission factors. Only traffic and industrial point sources were considered, without accounting for neither household nor commercial emissions [28].
The emission inventory was disaggregated over the Aburrá Valley (76
Local particulate matter emission inventories for the Aburrá Valley: (a) PM2.5, and (b) PM10. The values correspond with the estimated annual emissions. Taken from [
Two sets of low-cost sensors data were assembled: The first one included 255 sensors from the low-cost network that had a station from the official network within a 2-km radius. The second, higher quality one consisted of a subset of the previous set, including only those sensors whose data showed an
We performed four different LOTOS-EUROS simulations:
a LOTOS-EUROS model simulation without data assimilation (henceforth
a simulation with assimilation of data (observations) from the 14 stations of the official network (henceforth
a simulation with assimilation of the data from the entire low-cost network (henceforth
a simulation with assimilation only of high-quality data from the low-cost network (henceforth
The concentration fields were evaluated using seven of the official monitoring stations (
Temporal series of PM2.5 concentrations from selected validation stations of the official network, LOTOS-EUROS without assimilation, LE-official, LE-lowcost and LE-lowcost-HQ. Time stamps are valid for local time (UTC-5). A spin-up of 5 previous days was taken for each simulation. Taken from [
Figure 12 shows the diurnal cycles during the simulation period in the four selected validations stations. The diurnal cycle of the LE simulation differed from the observations in both magnitude and temporal behavior. The highest concentration peak that appears around 09:00 in all the stations is mainly due to traffic dynamics. In stations 25 and 88, the LE morning peak corresponded in time but not in magnitude with the observations; in stations 85 and 86, said peak appeared later in the simulations than in the observations. This time lag suggests a poor spatial representation of mobile emissions by the emissions inventory; or a deficiency it the wind fields in reproducing the valley dynamics, showing a late transport of the particulate material to these areas. The LE simulation did not capture the evening peak shown by the observations around 21:00 hours. The simulations using data assimilation presented diurnal cycles closer to the observations than did the LE simulation. The LE-official simulation captured the time and magnitude of the morning peak in stations 85 and 86. In station 88, LE-official corrected the time lag in the morning peak seen in LE, and improved the estimated magnitudes albeit still falling short of the observed values. A different behavior was seen for station 25, where LE-official had low diurnal variability, with a slight underestimation in the morning, and an overestimation in the afternoon. The LE-lowcost and LE-lowcost-HQ simulations results resembled closely the diurnal behavior of the observations, especially the temporal component. In all the stations, both the morning and the evening peaks matched the observations. The observed concentrations for stations 25 and 88 fell inside the standard deviation range for the LE-lowcost simulation; the same simulation overestimated the concentrations between 11:00 and 19:00 for station 85, and underestimated the concentrations between 01:00 and 13:00 for station 86. The LE-lowcost-HQ simulation results were overall the closest to observations.
Diurnal cycle of PM2.5 concentrations from selection stations of the official network, LOTOS-EUROS without assimilation, LE-official, LE-lowcost and LE-lowcost-HQ. The bars and the shadows represent the standard deviation over the simulation period. The time stamps are valid for local time (UTC-5). Taken from [
The averaged evaluation statistics among all the validation station are shown in Table 3. The simulation results without data assimilation (LE) underestimated the observed concentrations in all the validation stations. This was also seen in previous related works [24, 33]. The RMSE value reflected a low correspondence between the observed and simulated concentrations when using the model without data assimilation. The correlation coefficient was low, meaning that the model was not able to capture the variations in diurnal and day-to-day concentrations. In contrast, the three simulations using data assimilation had MFB values close to 0, without a significant difference among them. The data assimilation was thus effective in reducing between the model and reality. The RMSE also improved when using data assimilation, decreasing by 24.4% in the LE-official, 32.8% in the LE-lowcost, and 36.2% in the LE-lowcost-HQ simulations relative to the RMSE of the LE simulation. The
MFB | RMSE | ||
---|---|---|---|
−0.65 | 27.38 | 0.42 | |
−0.07 | 20.69 | 0.64 | |
0.08 | 18.39 | 0.76 | |
0.06 | 17.46 | 0.82 |
Mean fractional bias, root mean square error and Pearson correlation coefficient for simulated PM2.5. Values are averaged over all the validation stations for the simulation period.
We present a data assimilation application of a hyper-dense low-cost PM network and the chemical transport model LOTOS-EUROS in a urban setting. The low-cost network provided high quality data comparable to those provided by the official monitoring network. The performance of the model with assimilation of the spatially-dense data from the low-cost network improved both in terms of its representation of the observed dynamics, as well as in its forecast capabilities, highlighting its value as an air-quality management tool. Our results support the idea than with the current advances in the low-cost sensors, it is possible to use low-cost networks and data assimilation to model and predict air quality in urban areas.
Jointly with previous work [15, 18, 25, 37, 38, 39], our results can support and motivate the development of future low-cost networks and their integration in data fusion applications. According to the literature, North America, Europe, and China concentrate most of the current low-cost implementations, with experimental, citizen, and data dissemination purposes [14, 40]. In developing countries, a low-cost network, together with a CTM and data assimilation can provide a valuable first approach to monitoring PM without the high cost of an official air quality network.
Although one of the main advantages of a low-cost networks is the possibility of implemented hyper-dense networks with relative low costs, it is recommended to prioritize in the quality of the data (sensor quality, calibration, maintenance) and the study of optimal localization. High quality and the correct number and localization of sensors improve the data assimilation process and minimizes operational and computational costs.
The authors acknowledge the supercomputing resources made available by the Centro de Computación Científica Apolo at Universidad EAFIT (http://www.eafit.edu.co/apolo) to conduct this work.
The authors declare no conflict of interest.
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Important global efforts have been made to eliminate malaria leading to significant reduction in malaria cases and mortality in Africa by 42% and 66%, respectively. Early diagnosis, improved drug therapies and better health infrastructure are key components, but this extraordinary success is mainly due the use of long-lasting insecticidal nets (LLINs) and indoor residual sprayings (IRS) of insecticide. Unfortunately, the emergence and spread of resistance in mosquito populations against insecticides is jeopardising the effectiveness of the most efficient malaria control interventions. To help establish suitable resistance management strategies, it is vital to better understand the distribution of resistance, its mechanisms and impact on effectiveness of control interventions and malaria transmission. 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The purpose of this article is to outline the scope of low health literacy as a concept and explore some appropriate interventions that researchers and healthcare professionals may use to reduce its negative impact on health outcomes such as mortality. 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Despite the availability of information on the escalating adverse economic and health effects of NCDs globally, specific strategies designed to address the growing burden of NCDs in low-resourced countries remain substandard. Research engines like EBSCOhost, Science Citation Index, CINAHL database, PsycINFO, Cochrane Database of Systematic Reviews, published and unpublished abstracts and a hand search of reference lists and table of contents of relevant journals and books were searched from January 2011 to June 2019. In total, 84 studies met the inclusion criteria. Most studies confirm that low-resourced countries compared with high-resourced countries battle to implement NCDs prevention strategies; fail to record data on the risk factors of NCDs; medical records and surveillance data are unavailable. Due to a lack of knowledge and skill, low-resourced countries show no urgency to implement a systems approach for NCDs management. 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. 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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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Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/75036",hash:"",query:{},params:{id:"75036"},fullPath:"/chapters/75036",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()