Natural and anthropic sources of some elements and their industrial use (Source [1]).
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The intense exploitation of soil and the inadequate disposal of hazardous wastes by urban expansion, industrial and transport activities, mining, military activities and armed conflicts, and even unsustainable agricultural practices are the main sources of soil pollution. These anthropogenic activities release various chemicals into the environment that are often found to form a complex mixture of numerous contaminants. The different contaminants produce adverse effects on the health of ecosystems and all living beings that inhabit there. Moreover, the frequency and severity of extreme climatic events (droughts, floods, dust storms, and wildfires incidents) caused by climate change exacerbate soil contamination. Anthropogenic activities contribute to changes in the moisture and temperature regimes of soils and groundwater and can increase rates of movement of contaminants
Element | Essential | Natural sources | Anthropogenic sourses | Uses |
---|---|---|---|---|
Arseni | No | Dust storms Volcanic eruptions Geothermal/hydrothermal activity Forest fires Arsenic-rich minerals | Metal mining and smelting. Coal mining and burning of arsenic-rich coals. Pesticide. Timber industry. Pyrotechnics. | Wood preservatives. Additive to veterinarian drugs (poultry). Doping agent in semiconductors. |
Cadmium | No | Zinc and lead minerals. Phosphates rocks. | Electroplating. Metal industry (non-ferrous metals and steel). Automobile exhaust. Phosphate mineral fertilizer. | Pigments in paints, ceramics, plastics, etc. Cd impurities in Zn coatings used on metal structures. |
Chromium | Yes | Chromium minerals | Metal industry Electroplating. Industrial sewage. | Electroplating. Metal alloys. Anticorrosive products. Pesticides, detergents. |
Copper | Yes | Sulfides, oxides, carbonates | Domestic and industrial waste, mining waste, animal manure (pig and poultry). Car breaks. Metal industry. Copper-based fungicides. | Electric supplies, electric conductor. Electroplating. Fungicides. Plant residues treated with fungicides are used as soil amendments. Timber treatment chemicals. Copper piping and guttering. Vehicle brake linings. |
Lead | No | Lead minerals | Battery manufacturing facilities. Private and industrial waste. Rifle ranges and military facilities. Leaded paints and leaded fuel addition. Insecticides. | Batteries. Alloys, bullets and other munitions. |
Mercury | No | Mercury sulfide ores. Volcanoes. Forest fires. Ocean emissions. | Artisanal and small-scale gold mining. Chemical industry. Fossil fuels (coal and petroleum) combustion. Nonferrous metals production. | Catalysts, electrical switches. Batteries, fluorescent lights, felt production, thermometers and barometers. Alloys for dental fillings. Bright-red paint pigments. |
Nickel | Yes | Nickel minerals | Metal works, battery plants, electronics. Industrial waste. | Metal alloys, batteries, electronics. |
Zinc | Yes | Minerals | Battery plants. Metal industry. Phosphate fertilizers. | Batteries. Alloys. Construction anticorrosive planting. Tire rubber. Additives in veterinary drugs and pesticides. |
Natural and anthropic sources of some elements and their industrial use (Source [1]).
The insufficient registration of contaminated areas in many regions of the world and the lack of regulations for their remediation accentuate this environmental conflict. About 3.5 million sites in the European Union (EU) were estimated to be potentially contaminated, with 0.5 million sites being highly contaminated and needing urgent remediation. There are 400,000 polluted sites in European countries, including Germany, England, Denmark, Spain, Italy, Netherlands, and Finland. Sweden, France, Hungary, Slovakia, and Austria count up to 200,000 contaminated sites. Greece and Poland reported 10,000 contaminated land areas, while Ireland and Portugal reported less than 10,000 contaminated sites. In America, approximately 600,000-ha brownfield sites are polluted with heavy metals [2]. Identification and assessment of potentially polluted sites are the essential first step in the management of soil pollution.
Among the persistent and potentially (eco)toxic heavy metal(loids)s (HMs) ubiquitous around polluted soils are arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), lead (Pb), manganese (Mn), nickel (Ni), zinc (Zn), and radionuclides. Many of them are considered trace elements. The concentration of these HMs in soil has increased drastically over the last three decades, thus posing a risk to the environment and human health. A detailed description of the above-mentioned trace elements, including the natural and anthropogenic sources and uses, is given in Table 1. Identifying the sources of trace elements in the environment is of key importance to understanding the pollution patterns and natural global cycles, in addition to making decisions concerning soil pollution remediation.
The remediation methods are generally based on physical, chemical, and biological approaches, which may be used in combination with one another to clean-up HMs to an acceptable and safe level [3, 4, 5]. The physical and chemical conventional methods are usually expensive and can irreversibly affect the properties of soil, water and the living beings that inhabit them [6]. Figure 1 resumes the soil remediation techniques based on chemical, physical, and biological processes developed during the last two decades [4, 7, 8, 9].
Comparison of physical, chemical and biological methods of remediation for polluted soils or contaminated substrate. Physical remediation methods include (1) soil replacement, (2) soil isolation, (3) vitrification, and (4) electrokinetic; biological methods generally include (5) phytovolatilization, (6) phytoextraction and (7) phytostabilization; chemical methods contain (8) immobilization and (9) soil washing. Biological and chemical methods can be applied jointly depending on the type of contaminant, soil, plant and chemical reagent. Moreover, the effectiveness of different phytoremediation techniques can be enhanced by microbial-, chelate- and genetic-assisted remediation. Modified from [
These methods consist of removing or reducing contaminants by physical methods such as dilution, heating, and solidification of contaminated soil. Some of the technologies involve the soil replacement or isolation, the thermal analytical method, vitrification, and the electric repair technique, which does not change the chemical properties of the pollutants.
Phytoremediation technology:
Phytoremediation involves the use of plants to extract and remove chemical pollutants or to decrease their bioavailability in soil [12, 13]. In general, plants used to carry out phytoremediation are known as metallophytes. The main benefits reported for phytoremediation include less secondary waste generation and minimal-associated environmental disturbance
These methods include soil washing and immobilization technologies. Different chemicals or solvents (metallic oxides, clays, or biomaterials) are added into the soils to stabilize the pollutants and convert them into less toxic forms to living organisms, thus reducing their bioavailability, adsorption, or transformation [27]. The remediation chemical methods are faster than biological ones and could also be applied
As it was previously mentioned, the bioavailability and mobility of HMs in soil substrate are greatly influenced by the soil physicochemical properties (pH, Eh, electrical conductivity, cation exchange capacity, and soil mineralogy), the biological conditions, and the presence of soil inorganic and organic ligands. Careful risk assessments should be undertaken to select the appropriate hyperaccumulating plant species and determine safe and acceptable use of the aboveground plant biomass. As this aerial plant biomass gradually accumulates trace elements and other contaminants and its toxicity is likely to increase, it is important to select those hyperaccumulating species that are unlikely to enter the food chain or implement a protection system to avoid this important issue. There are several post-harvest management options for crops including energy generation, biofuel production, gasification, composting, recovery of critical and secondary raw material recovery, and phytomining.
Microbial-assisted phytomining of HMs also represents a promising method for the remediation of contaminated soil [30]. Microbial-assisted phytomining of HMs involves several mechanisms such as biosorption, intracellular accumulation, enzyme-catalyzed transformation, bioleaching and biomineralization, and redox reactions [31]. In many cases, plant-microbe associations are highly efficient in absorbing, accumulating, translocating, and tolerating HMs because of their capacity to produce various substances that participate in stimulating growth and HMs accumulation (monocyclopropane-1-carboxylate deaminase, siderophores, indole acetic acid) [30]. In microbial-assisted phytomining, the exudates of mycorrhizal roots play a significant role in the efficiency of phytoextraction of the elements in the soil. For instance, concentrations of amino acids (glutamine, glutamic acid, valine, and methionine) and organic acids (citric acid, malic acid, and oxalic acid) in the root exudate of
Several authors have reported the operating costs related to different remediation technologies, normalized per unit (m3) of contaminated soil (Table 2).
Applied technology | Cost/ m3contaminated soil | Observations | Source |
---|---|---|---|
Phytoremediation (phytoextraction) | US$ 37,7 | Biological (20 cm soil depth, 2 years), initial capital included in the cost. | [36] |
US$ 50–200 | Biological (density 2 tn/m3) | [37] | |
US$ 10–35 | Biological | [3] | |
Plant extraction | US$ 19–78 | Physical-biological | [38] |
Phytostabilization | US$ 1.3 | Biological | [1] |
Turnover and attenuation | US$ 4.7–5.6 | Physical | [38] |
Extraction | US$ 240–290 | Physico-chemical | [36] |
Solidification | US$ 87–190 | Physico-chemical | [36] |
US$ 480–813 | Physical | [39] | |
US$ 81–252 | Physical | [40] | |
US$ 130–260 | Chemical-Biological | [36] | |
US$ 100 | Biological, initial capital included in the cost | [41] | |
Unlined repositories | US$ 9.52 | Physical | [42] |
Lined repositories | US$ 34.44 | ||
Soil replacement—excavation | US$ 540–920 | Physical ( | [43] |
Excavation and treatment | US$ 145 | Physical-chemical, initial capital included in the cost | [1] |
Vitrification | US$ 600–1000 | Physical, initial capital included in the cost | [37] |
Flushing | US$ 150–420 | Physical | [37] |
US$ 50–150 | Biological | [3] | |
Bioremediation | US$ 50 | Biological | [1] |
Stabilization/solidification | US$ 240–340 | Chemical | [3] |
Soil venting | US$ 20–220 | Chemical | [3] |
Solvent extraction | US$ 360–440 | Chemical | [3] |
Soil washing | US$ 80–200 | Chemical | [3] |
Incineration | US$ 200–1500 | Chemical | [3] |
Phytoextraction+ Chelation | US$ 15 | Chemical-biological | [44] |
Economical costs of some technologies for remediation of contaminated soils.
As Table 2 shows, phytoremediation represents a sustainable and low-cost alternative for the rehabilitation of environments affected by natural and anthropogenic pollutants [45, 46]. The differential costs reported among similar methodologies are concerned with the type of chemical elements and their concentration to be extracted, the technical procedures, the type and amount of soil to be remediated, the area to be treated, and the ideal nontoxic concentration value of pollutants to be achieved, among others. For some instances, only high-costing operations are considered, and it is frequently that a plus initial capital has been added for carrying out remediation.
Based on economic implications, the aim of phytoremediation can be three-layered: (1) phytomining (plant-based extraction of metals with a financial benefit, i.e., in the perspective of critical and secondary raw materials recovery from plant biomass [47]); (2) minimization of the risks of bioaugmentation of contaminants in the food chain, for example, by stabilization of Cd in cocoa plantations (https://www.fontagro.org/new/proyectos/bioproceso-cd/en); and (3) sustainable soil management by steadily increases soil fertility allowing for follow-up cultivation of crops with added economic value [48, 49, 50]. In concordance, the chelate-assisted and microbial-assisted phytoextraction and use of genetically engineered plants can further reduce the cost of remediation by enhancing metal accumulation and decreasing remediation time. Moreover, the operational costs remain the same as for phytoextraction alone. The implementation of phytoremediation as an effective methodology that guarantees the recovery of elements of interest and the rehabilitation of the soil must contemplate, in the long term, a period greater than 4 months for continuous monitoring, to ensure not having negative impacts due to external and internal variables (e.g., climatic variables, man, animals, changes in pH, Eh) that may affect the efficiency of the process [34, 36, 47].
—Constructed wetland systems:
From the remediation conceptual tests at the laboratory corresponding to technology readiness level (TRL) 1–2 to their applications in the territory (TRL 7), a long way of calibration and adjustments must be executed. Generally, a significant economic loss is given by poor evidence of adaptation and adjustment when technology proceeds from TRL 3 to TRL 7 [51].
In the study by Scotti et al. [34], a constructed wetland system called vegetable depuration module (VDM) is proposed as a calibrator of variables in MAP tests (Figure 2A). The use of VDM allows to determining the balance mass and the metal(loid) partition between soil, fungal structures, mycorrhizal roots, and aboveground plant tissues. The VDM allows the leaching of different HMs under particular conditions of pH-Eh, organic matter and other amendments and co-enzymatic factors (among other elements) taking to account the hydraulic variables such as type of irrigation (vertical, horizontal, continuous, interrupted, laminar, or turbulent), dynamics flow, and constant of hydraulic retention (Kh physical constant dependent on filling). Partitioning among different media usually relies on an equilibrium between the contaminant adsorbed on solid surfaces and the contaminant dissolved in a liquid (or gaseous) phases, controlled by the chemical characteristics of the contaminant (e.g., hydrophobicity, volatility). Several distribution coefficients have been developed over the years (e.g., partitioning coefficient between soil and water: Kd, organic carbon and water: Koc, or octanol-water partition coefficient: Kow) to elucidate processes in nature, but these are usually simple models that do not consider the specificity of sorption sites or competition among molecules and elements [1]. Thermodynamic processes that determine the bioavailability of trace elements are complex, and VDM allows to calibrate some of these processes. Once the calibration of these parameters has been obtained, the system can be scaled up to territory by adapting the engineering practices. The description of the VDM [34] shows it as a modified subsurface constructed wetland that allows designing the type and quantity of underground filter, its granulometry, type of substrate, and amendments besides the hydraulic system.
A: The Vegetable Depuration Module (VDM) under construction, B: Vertical flow beds (VFB) under construction in Lima, Peru; C: VFB under construction in Bayawan City, Philippines; D: From left to right: three VFBs (filters) for pre-treatment and two VFBs for secondary treatment in Albondón, Spain (photos by (photos from [
The VDM (located at
The VDM is a technological development adaptable to different designs and methodologies with a scale of TRL 6 as a simulated environment. The output of the VDM calibration corresponds to the first engineering cycle of a design to be taken to field scale. Under the experimental conditions in the VDM, the HMs in multi-contaminated soils with high leaching properties pass to the collecting chamber to be recycled and treated in another VDM with different physical-chemical and biological conditions. Consequently, those HMs translocated to plant biomass are considered bio-extracted and the elements retained in the substrate without entering biomass are considered stabilized. Furthermore, the VDM allows calibration of the capacity of phytoextraction or phytostabilization of a given system under certain conditions. The differential behavior between phytoextraction and phytostabilization is mainly given by the soil conditions and the plant-microorganism association. The mycorrhizal plants can retain HMs in soil substrate by physical-chemical fixation, redox reactions, absorption and adsorption in the extra-radical mycelium and spores, and by releasing glomalin, a complex of glycoproteins that acts as a carbon reservoir in soils and is involved in the sequestration of HMs [53]. Recent studies have demonstrated that AM symbiosis performance can fluctuate between phytostabilization and/or phytoextraction depending on certain HMs, the environmental conditions, and the types of plant and fungal partners [54]. As it is known, the bioavailability of HMs is related to the solubility of these elements, which intimately depends on the temperature, pH, and Eh parameters (Pourbaix) [55], among other factors. The VDM allows modifying the retention capacity in the substrate or the leaching rates of HMs by controlling the pH-Eh values according to the soil-plant-microorganisms system applied.
Recently, different modular constructed wetland systems in series were designed with different numbers of vertical flow bed (VFB). In the system designed in Figure 2B, the entire surface is used as an inlet area to greywater influents through connected pipes with uniform holes that later are covered with gravel to complete the testing performances. In Figure 2C, another example of a VFB is constructed for the treatment of wastewater from a landfill. Figure 2D shows modular constructed wetland systems in series without electricity supply as it is built on a slope. It consists of three VFBs for pre-treatment as a filtration step and two VFBs for secondary treatment.
In Scotti et al. [47], we estimated the calibration for an efficient extraction of CRM and SRM per m3 of mining soil treated in the VDM with the MAP system. Estimated bio-extracting potential (BP) was in the range 2.417 g (K) > BP > 0.14 g (As) per m3 of contaminated soil, suggesting the eventual subsequent recovery of SRMs and CRMs by hydrometallurgical techniques, with final purification by selective electrodeposition, as a viable and cost-effective option. In this work, the costs of a projection to TRL 7 (real environment) of the BP results reached by using the MAP and the VDM were determined. For that, an economic model used by Wan et al. [36] was followed, separating initial capital costs and operating costs. Also, repositories and constructed wetland costs were considered (Table 3). For the operating costs, various models of repositories and constructed wetlands were taken into account depending on the objective to be achieved [34, 52]. The costs of the projection to the territory for the application of MAP using designed models of the VDM are shown in Table 3. The costs of the MAP system were divided into initial capital and operational costs. The initial capital includes the following items: investigation about pollution, selection of remediation strategy, soil preparation, construction of modules repositories, pipes and collector chambers, equipment, temporary store, irrigation system, and incineration equipment. Construction of adequate accesses is required.
Items | Cost (USD)/ha | ||
---|---|---|---|
Strategy selection | 824.8 | ||
Modules, collector chambers, repositories | 5770.3 | ||
Equipment | 5893.6 | ||
Irrigation system | 5986.8 | ||
Possible accesses required | 4548.4 | ||
Incineration equipment | 7216.5 | ||
Others | 3812.4 | ||
Cost of labor | Seedling | 0.082 | |
AM fungal inocula production | 0,390 | ||
Transplant | 0.103 | ||
Fertilize | 0.062 | ||
Insect control | 0.062 | ||
Irrigation and recycled | 0.062 | ||
Weed control | 0.206 | ||
Harvest | 0.093 | ||
Module filling, placement of stones, amendments, chelates, others | 0.329 | ||
Cost of materials | Seedling tray | 0.041 | |
Hyperaccumulator seedlings | 0.082 | ||
Crops seedlings | 1.261 | ||
Farm chemicals | 0.021 | ||
Fertilizer | 7.446 | ||
Stone filter, amendments, chelates, others | 0.461 | ||
Cost for usage of large machines | Harvest machines | 0.148 | |
Incineration machine | 0.161 | ||
Disposal of dangerous wastes | 0.103 | ||
Other direct cost | Production compensation | 0.178 | |
Rent of land | 0.155 | ||
Fuel and power cost | 0.974 | ||
Construction supervision | 0.037 | ||
Environment supervision | 2.006 | ||
Regular monitor | 1.650 | ||
Indirect cost | Staff wage | 0.495 | |
Administrative expenses | 0.412 | ||
Travel expenses | 1.944 | ||
Cost of water and electricity | 1.003 | ||
Others (amortizations, taxes) | 3.782 | ||
17.026 | |||
Costs of projection to the territory for the application of MAP using designed models of the VDM.
Regarding the operational costs, they include the cost of labor and materials, large machines, and other direct or indirect costs. The cost of labor involves seedling, production of AM fungal inocula, transplantation, fertilizer application, insect control, irrigation and recycled, weed control, harvesting, module filling, placement of stones, amendments and chelates, and some other less significant items. The cost of materials includes the purchase of seedling tray, hyperaccumulator seedling production, crop seedlings, farm chemicals, inorganic and organic fertilizer, stones filter, amendments, chelates, and some other less significant items. The cost of using large machines includes rent for machines during harvest, incineration, and disposal of dangerous wastes. The direct cost is the production compensation and rent of land, which are paid to the local farmer; fuel and power cost during the phytoremediation project; construction and environmental supervision, amortization for the initial capital to 10 years. When the land is fiscal (government), the compensation is included in the indirect costs at the level of tax rates. A conversion from ha to m3 was carried out taking into account 0.2 m of soil depth. The total estimated cost of MAP was US$ 40.775 with initial capital and operational costs accounting for 41.76% and 58.24%, respectively. On the other hand, the operational cost in total was US$ 23.75. It is highlighting that the cost for labor is low compared to total operating costs. This could indicate that the system is simple to be managed, and no extreme technical skills are required to handle it.
In Table 4, the commercial value of each chemical element established by the global market was linked to the quantities of each bioextracted element in the VDM corresponding to 1 m3 of treated soil substrate. Although the commercialization value corresponds to the last prize quote, we observed that there are elements (Mn, Fe, P, Rb Sr., Al, Ba, K, S, and K) that are highly remunerative, and their cost of bioextraction is very low (US$ 40.75/m3), disregarding the cost of hydrometallurgy to recover metal with high purity. Therefore, an important aspect in applying bioextraction processes is the appropriate selection of the experimental conditions, the combination of chemical elements, the adding of amendments and enzymatic co-factors, and an efficient mycorrhizal hyperaccumulating plant.
Element | Purity (%) | BP g/VDM (m3) | State | Price USD/g (Market) | Recovery (USD) in VDM (m3) | Source |
---|---|---|---|---|---|---|
Mn | 99.7 | 34.82 | scales | 0.15 | 5.223 | [56] |
Fe | 99.99 | 60.01 | powder 450 μ | 1.19 | 71.4119 | [57] |
Ga | 99.99 | 1.02 | spheres | 30.97 | 31.5894 | [58] |
P | 99.5 | 114.7 | red | 91.72 | 10,520 | [59] |
As | 99.99 | 0.145 | lump | 40.78 | 5.91 | [60] |
Zn | 99.5 | 43.22 | Ingot | 0.023 | 0.99 | [61] |
Ti | 99.95 | 2.2 | Ingot | 0.16 | 0.352 | [62] |
Cr | 99.5 | 0.21 | Ingot | 1.2 | 0.252 | [63] |
Ni | 99.99 | 0.22 | spheres | 0.14 | 0.0308 | [64] |
Cu | 99.99 | 0.55 | tabs | 0.12 | 0.066 | [65] |
Rb | 99.99 | 3.39 | tabs | 421 | 1.427 | [66] |
Sr | 99.8 | 12.14 | dendritic pieces | 9 | 109.26 | [67] |
Al | 99.99 | 23.99 | granules | 1.8 | 43.182 | [68] |
Ba | 99 | 0.65 | lump | 348.4 | 226.46 | [69] |
K | 99.97 | 2,417 | lump | 8.47 | 20471.99 | [70] |
S | 99.8 | 243.9 | powder | 11.29 | 2.754 | [71] |
Ca | 99 | 690 | lump | 29.76 | 20.534 | [72] |
The commercial value of each chemical element established by the global market linked to the quantities of each bioextracted element in the VDM corresponding to 1 m3 of the treated soil substrate.
Sustainable remediation is now covered by the International Organization for Standardization with the ISO standard 18504:2017 “Soil quality – Sustainable remediation” [73]. In the United States of America, the international American Society for Testing and Materials (ASTM) has developed the “Standard Guide for Greener Clean-ups ASTM E2893 - 16e1” [74]. Australia has developed a National Remediation Framework and technical guidance to support its legislation on polluted sites [75]. In this regard, ISO Technical Committee on Soil Quality has developed a valuable catalog of standard methods for the analyses of soil contaminants, as well as the design and implementation of soil sampling from contaminated sites. ISO also includes methods to assess the toxicity of contaminated soils to plants, microorganisms, earthworms, insects, and other biota [56]. However, this extensive catalog is only available on a standard purchase basis, making it difficult to access, especially in developing countries. To facilitate universal access to internationally developed and agreed standards, the Global Soil Partnership works in collaboration with experts from around the world to identify, agree, and make harmonized sampling and analytical procedures available worldwide through the global networks of soil laboratories (GLOSOLAN1, http://www.fao.org/global-soil-partnership/glosolan/en/) and soil information institutions (INSII2, http://www.fao.org/global-soil-partnership/insii/en/).
Phytoextraction is a safe, least destructive, eco-friendly, and cost-efficient remediation technique that allows soil clean-up over a large scale. The cost of phytoremediation of HMs contaminated soils can be minimized by better understanding the mechanisms and processes involved in bioremediation, and the many options at the different remediation steps.
Phytomining is an incipient methodology for both remediation and recovery of chemical elements of interest. The UE in its 4th list [76] declares 20 critical raw materials due to their availability in nature and the increasing demand in the industry. Many of these elements can be recovered in toxicity-tolerant hyperaccumulators plants.
This methodology can be improved by modulating the physical-chemical and biological variables and their dynamism. For instance, amendments, enzymatic co-factors, and chelators could be incorporated by both artificially and naturally ways to set physical-chemical variables. But results about biological exudates are not constant and reproducible because they depend on an elapsed time, nutrients, and microorganisms present in soils.
Variables on phytomining techniques are currently under study, and many of these have not been elucidated yet, leading to failures when the technology is scaled up. To carry out this calibration, we propose the application of the VDM at a TRL 6 scale (1 to 10 m3 of soil) before taking it to the territory (TRL 7). Through the controlled experiences in the VDM, it is possible to obtain information on phytoextraction, phytostabilization, and leaching of the elements under study.
In this sense, to successfully transfer this methodology to territory, we can generalize the knowledge about the partition of a certain chemical element: a) root and aerial biomass (translocation factor), b) root and soil biomass (bioconcentration), and c) solid-liquid matrix phases. These partition compartments are dependent on various physical-chemical and biological factors.
Regarding the economic aspect, phytoremediation is a very convenient option compared to other techniques of remediation used. In turn, the possibility of recovering valuable chemical elements for the global market, this methodology becomes even more convenient.
Finally, the social license for phytoremediation, under recovery of commercially important chemical elements and minimization of wastes in the environment, makes this methodology a good option toward a circular economy.
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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. 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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. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"87",type:"subseries",title:"Economics",keywords:"Globalization, Economic Integration, Growth and Development, International Trade, Environmental Development, Developed Countries, Developing Countries, Technical Innovation, Knowledge Management, Political Economy Analysis, Banking and Financial Markets",scope:"
\r\n\tThe topic on Economics is designed to disseminate knowledge around broad global economic issues. Original submissions will be accepted in English for applied and theoretical articles, case studies and reviews about the specific challenges and opportunities faced by the economies and markets around the world. The authors are encouraged to apply rigorous economic analysis with significant policy implications for developed and developing countries. Examples of subjects of interest will include, but are not limited to globalization, economic integration, growth and development, international trade, environmental development, country specific comparative analysis, technical innovation and knowledge management, political economy analysis, and banking and financial markets.
",coverUrl:"https://cdn.intechopen.com/series_topics/covers/87.jpg",hasOnlineFirst:!1,hasPublishedBooks:!1,annualVolume:11971,editor:{id:"327730",title:"Prof.",name:"Jaime",middleName:null,surname:"Ortiz",slug:"jaime-ortiz",fullName:"Jaime Ortiz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002zaOKZQA2/Profile_Picture_1642145584421",biography:"Dr. Jaime Ortiz holds degrees from Chile, the Netherlands, and the United States. He has held tenured faculty, distinguished professorship, and executive leadership appointments in several universities around the world. Dr. Ortiz has previously worked for international organizations and non-government entities in economic and business matters, and he has university-wide globalization engagement in more than thirty-six countries. He has advised, among others, the United Nations Development Program, Inter-American Development Bank, Organization of American States, Pre-investment Organization of Latin America and the Caribbean, Technical Cooperation of the Suisse Government, and the World Bank. Dr. Ortiz is the author, co-author, or editor of books, book chapters, textbooks, research monographs and technical reports, and refereed journal articles. He is listed in Who’s Who in the World, Who’s Who in America, Who’s Who in Finance and Business, Who’s Who in Business Higher Education, Who’s Who in American Education, and Who’s Who Directory of Economists. Dr. Ortiz has been a Fulbright Scholar and an MSI Leadership Fellow with the W.K. Kellogg Foundation. 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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/21811",hash:"",query:{},params:{id:"21811"},fullPath:"/chapters/21811",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)}()