Classification of kinetic models.
\r\n\tThis volume presents the multifaceted aspects and should allow readers at all levels an entry into the exiting world of Chlamydomonas research.
\r\n\t
Industrialization and urbanization have promoted the generation of great quantities of aqueous effluents that may contain high levels of toxic compounds [1]. Every day, 2,000,000 tons of wastes (from sewers or agricultural and industrial residues) are released into rivers and seas, spreading disease and damage to ecosystems. Achim Steiner, executive chief of the United Nations Program for the Environment stated: “If the world is to thrive, let alone to survive on a planet of 6 billion people heading to over 9 billion by 2050, we need to be collectively smarter about how we manage waste, including wastewaters” [2].
Heavy metals, or potentially toxic elements, constitute a specific group of pollutants that are released into the environment as a result of industrial activities, such as the mining industry. These elements can cause health problems. In México, the mining industry is one of the most important economic activities, with gold, silver, and copper being the precious metals with higher production rates [3].
The metallurgical process of the mining industry involves a series of extraction and purification techniques that result in the disposal of metals into water bodies through acid mine drainage (DAM). Heavy metals can then accumulate at toxic concentrations for a functional ecosystem, which constitutes an economic problem of public health [4].
Controlling and reducing water pollution is a significant concern for our society. Wastewater spills create eutrophication and toxic problems. The wastewater penetrates the soil, contaminates groundwater, and reduces the quality necessary for human consumption [5].
Discharge limits have been established for heavy metals, among many other water pollutants. Most heavy metals are soluble and form aqueous solutions; hence, they cannot be separated by ordinary physical treatments [6].
Contamination of soil and water is the result of numerous industrial activities such as mining, melting, fabrication of jewelry, batteries, and automobiles, and volatile ashes from incineration processes. This type of contamination poses a serious threat for human and animal health since heavy metals remain in the environment for an indefinite time [7].
México has several sites contaminated by heavy metals and other residues from the mining industry. A particular example of pollution is found in the San Pedro River, located in the state of Sonora, México, where silver and copper production has been exploited for decades. The San Pedro River stream originates near Cananea – a mining town known for having the biggest mining districts of the state – and culminates in the state of Arizona, in the United States. Surface water pollution in the San Pedro river was reported in 1997 and 1999 [8]. In 2008, the presence of heavy metals in the river sediments was also evaluated [9]. The river has been contaminated by heavy metals due to its proximity with the metallurgical activity of the state. Metals found in the river are: cadmium, cobalt, chromium, iron, manganese, copper, zinc, nickel, and lead. However, two of the metals with higher concentrations were copper and iron, which exceeded the maximum permissible values established in the Mexican laws for water quality. These laws consider lead, zinc, mercury, silver, nickel, cadmium, aluminum, copper, and arsenic, as water pollutants due to the toxicity they pose for aquatic and terrestrial organisms (NOM-001-ECOL-1996; NOM-002-ECOL-1996; NOM-003-ECOL-1996).
More recently, on August 7, 2014, the Buenavista Copper Mine in Cananea was under the spotlight when approximately 40,000 cubic meters of sulfuric acid were spilled into the Bacanuchi River (also situated in Sonora). This toxic leakage affected an estimate of about 800,000 people [10]. Heavy metals pollution has been reported, but the remediation projects aiming to recover the quality of these sites have been extremely scarce. Thus, it is of great importance for research institutions and industries to evaluate technological alternatives for the removal and stabilization of inorganic contaminants, keeping into consideration the specific environmental conditions of each polluted site [11].
The removal of heavy metals can be carried out by a number of conventional treatments, such as reverse osmosis, electrodialysis, ultrafiltration, chemical precipitation, and ionic exchange. These methods, however, have the disadvantage of requiring high operation costs. The ionic exchange resins, for example, have been commercially known for their effectiveness as pollutant adsorbents in wastewater treatments, but their high cost hinders their application at industrial levels [1]. Chemical processes, although simple to perform, end up being even more expensive because of the active agent that cannot be recovered for future uses. Besides, the final product is a high concentrated sludge difficult to handle [4].
Heavy metals sources are not renewable, and the natural reserves are being consumed. Therefore, it is imperative that those elements considered dangerous to the environment or those of technological importance and economic value are withdrawn and recovered at their point of origin through appropriate treatments.
A brief description of the before mentioned processes is presented next:
Reverse osmosis: a process where heavy metals are separated through a semipermeable membrane by using a pressure higher than the osmotic pressure, which is caused by the dissolved solids in wastewaters. The high pressures required for this process are the main reason for the high operating costs of reverse osmosis.
Electrodialysis: in this process, metallic ions are separated by selective semipermeable membranes. An electric current is applied between two electrodes located at each side of the membranes, which produces a migration of cations and anions toward their respective electrodes. The migration of ions results in the formation of metal salts that precipitate out of solution. However, a major disadvantage of electrodialysis is membrane clogging, caused mainly by the formation of metal hydroxides.
Ultrafiltration: this process involves the use of porous membranes and high pressures for the separation of metal ions. Sludge generation is the main disadvantage in this treatment.
Ionic exchange: metallic ions in diluted solutions are exchanged with the ions located in the active sites of synthetic resins by electrostatic forces. Sludge generation and the high costs of exchange resins are the main disadvantages.
Chemical precipitation: precipitation of metallic ions is achieved by the addition of coagulants such as calcium salts, iron, and other organic polymers. The inconvenience of this method is the excessive amounts of sludge (it might include toxic compounds) produced during the precipitation.
Phytoremediation: it involves the use of certain plants as removing or stabilizing agents in contaminated soils, sediments, and water. The time required for effective stabilization of heavy metals is large and can be a constraint in this process; furthermore, plant regeneration is even more complex.
All of the disadvantages previously mentioned, such as incomplete removal, high energy consumption, excessive residual sludge, and formation of other toxic residues requiring careful disposal protocols justify the need for a cost-effective treatment for the removal of heavy metals from wastewater [12].
New technologies are currently being developed, taking into consideration the processing costs and direct scaling up and implementation [13]. The search for effective removal technologies has directed attention toward biosorption, an ecological alternative that uses different biological materials for binding and concentrating metal ions.
Biosorption: This process is based on the capacity of biological materials to concentrate heavy metals by either metabolic or physical–chemical pathways.
Developments in the field of environmental biotechnology have allowed the identification of several species of algae, bacteria, fungi, and yeast as effective metal biosorbents [14]. The main advantages of biosorption over conventional treatments include: lower costs, high removal yields, minimum residual sludge formation, and potential biosorbent regeneration and metal recovery [15].
The biosorption process involves a solid phase – the biosorbent, or biomass – and a liquid phase – the solvent (commonly water). The liquid phase contains the sorbate, i.e., the species to be sorbed (metallic ions). During biosorption, the sorbate is attracted and bound to the biosorbent through a variety of mechanisms. This “binding” process continues until a state of equilibrium is achieved between the amount of sorbate present and the available active sites of the biosorbent [16].
The two mechanisms by which biosorption can take place are [13]:
Bioaccumulation: based in the intracellular transport of metallic ions by living biomass.
Bioadsorption: based on the adsorption of metallic ions on the cell surface. This process can occur by ionic exchange, precipitation, complexation, or electrostatic attraction. Figure 1 shows a basic experimental approach that can be used to determine the biosorption capacity, q, a measure of the metal uptake by biomass.
General experimental setup for biosorption of heavy metals.
The biosorption process can be carried out in a bioreactor, where the wastewater flows through a bed of microorganisms which bind the heavy metals. Bioreactors are useful tools where high volumes of wastewaters may be treated continuously, transferring the contaminated “portion” to a considerable smaller volume. However, certain problems can arise during the operation of bioreactors, such as biomass washout, liquid–solid separation difficulties, and pressure drops. These problems originate due to the fact that microbial biomass generally consists of small particles with low density and poor mechanical strength [17]. Immobilization of biomass in a suitable matrix (or material supports) can overcome washout problems by inducing cellular growth in the form of a stable biofilm constituted by microbial cells and extracellular polymeric substances.
Nowadays, the use of microorganisms for environmental remediation and recovery purposes has grown as a research field. It is believed that the most fitted microorganisms for removal treatments are the ones isolated from the same environment where they were naturally selected; however, genetic manipulation techniques can be used to enhance the capacity of different microorganisms [18].
Bioremediation utilizes the catalytic abilities of living organisms to degrade and transform pollutants from aquatic and terrestrial ecosystems. This alternative can be potentially applied to mitigate environmental contamination. Bioremediation has focused on the exploitation of genetic diversity and metabolic versatility, characteristic traits that make bacteria suitable for the transformation of pollutants into harmless products, or less toxic compounds, that can be reintegrated in the natural biochemical cycles. On the other hand, there are other microorganisms such as fungi or plants that have been isolated and used in removal processes like phytoremediation [19].
Microorganisms are naturally exposed to heavy metals in essential or toxic quantities, and the amount of heavy metals in certain sites can be so high that microorganism growth is not possible. Metal toxicity forces microorganisms to develop various strategies to defend themselves against high concentrations of heavy metals [20].
There are several experimental protocols important to effectively examine metal biosorption by aerobic biomass. These protocols are described below.
Isolation is used to identify microorganisms able to grow in polluted environments. Wastewater samples are generally collected from damaged sites, and yeast or bacteria (biomass) cells are grown by inoculating them into a nutrient-rich environment. Inoculation is usually done in cell-culture dishes by the streaking method using selective enriched nutritive media for each microorganism. Commonly, 10 mL of wastewater sample is inoculated in a specific culture medium at 37°C for 24 h.
The biosorption batch tests with aerobic biomass are carried out in experimental vessels, such as Erlenmeyer flasks. Wastewater samples are mixed with a known amount of biomass. Flasks are placed in an incubator at specific conditions and tests are carried out in duplicate, using two flasks for every sampling time. For aerobic microorganisms such as yeast, the conditions are usually set as follows: pH 3–4, 37°C, and 100 rpm. Samples are taken at regular intervals until equilibrium is achieved. Every sample is then centrifuged to separate biomass from the solution. Concentration of metals is usually determined by atomic absorption spectrometry.
Biosorption efficiency (E) is calculated as follows:
where:
Co, Cf are the initial and final metal concentration (mg/L).
The biosorption capacity of the biomass at any given time is calculated as follows:
where
mo is the initial mass (mg), equal to the initial concentration (mg/L) times initial volume;
meq is the mass at equilibrium (mg), equal to the concentration (mg/L) times volume at equilibrium;
vads is the volume of biomass used (L).
Continuous studies are carried out in bioreactors. Bioreactors consist commonly of acrylic or glass columns with lateral sampling points. Perhaps, the simplest configuration is the Upflow Aerobic Reactor packed with material supports and biomass recirculation. An example of material support is clinoptilolite, a zeolite with a particle size of 4.76 mm, a pore diameter of 3.22E–03μ m and a Si/Al ratio of 4.53. Aerobic conditions are met by supplying air from the bottom of the column through peristaltic pumps.
Once reactors are inoculated with the selected aerobic biomass, mineral medium is used for biomass acclimation at pH levels optimum for growth. Mineral medium is only used during startup as a source of nutrients for biomass growth and immobilization. In the case of yeasts, pH is generally 3–4, and the medium consists of the following compounds: (g/L): ammonium phosphate 1, glucose 5, sodium chloride 5, magnesium sulfate 0.2, and phosphate potassium 1 [21].
Figure 2 shows a schematic diagram of two Upflow Aerobic Reactors connected in series that were used to remove heavy metals by Hernández-Mata et al., 2014 [22]. In this scheme, the first reactor (R1) was inoculated with biomass and the effluent was recirculated until the biomass reached a concentration of 1 g/L. When the desired biomass concentration was achieved, the biosorption stage was initiated with mining effluents. After the biosorption stage, a desorption (purification) step was carried out to remove the metallic ions adsorbed by the biomass. Biomass concentration was measured once again until the concentration reached 1 g/L. The effluent of R1 was then fed to R2 (containing the same biomass produced in R1) and biosorption was examined in both reactors. Samples were taken at regular intervals at the inlet and outlet points until column saturation was evident [22].
Schematic diagram of two upflow aerobic reactors packed with zeolite.
Anaerobic microorganisms perform as part of their metabolism a process known as anaerobic digestion, which has been widely implemented in the treatment and stabilization of effluents with high organic loads. Two of the main bacterial groups that participate in anaerobic digestion are acidogenic microorganisms (responsible for the conversion of organic matter into volatile fatty acids, VFAs) and methanogenic microorganisms (methane producers).
Generally, it is considered that methanogenic bacteria are less resistant to external changes in their growing conditions such as pH, temperature, and/or presence of toxic metals [23]. It was also reported in a previous study that inhibition by heavy metals was less noticeable for acidogenic bacteria [24].
To achieve acidogenic conditions, biomass can be inoculated in Erlenmeyer flasks for a large period of time (up to 8 weeks), mixing anaerobic sludge and material supports (if desirable). The flasks are kept at 30°C. The feed medium is changed continuously and prepared according to the requirements of the microorganism [25]. The medium pH is kept at acidic levels (3–4) to inhibit the growth of methanogenic organisms, which is favored at neutral pH.
Dextrose is generally used as substrate. This substrate is the source of organic matter that enhances volatile fatty acids (VFAs) formation, mainly: acetic acid, propionic acid, and butyric acid. In order to verify that the anaerobic sludge is carrying out the acidogenic phase of digestion, VFAs formation and concentration can be measured by HPLC (high performance liquid chromatography) taking samples from the flasks at regular intervals. pH can be measured daily and the growth of biomass can be indirectly calculated by determining the volatile suspended solids (VSS), which are obtained according to the gravimetric method [26].
Toxicity tests are carried out prior to any biosorption test with living biomass to obtain inhibitory concentrations. For acidogenic biomass, VFAs formation or substrate consumption are a direct measurement of microbial activity. During a toxicity experiment, a known amount of biomass (or immobilized biomass, if desirable) is put into a series of flasks and mixed with fixed volumes of metallic solutions and a selected substrate. The concentration of heavy metals in the metallic solutions varies according to each experimental setup and metallic ion, but one flask must be selected as a blank. The concentration of the organic substrate is kept constant in all flasks. Solution pH has to be adjusted to acidic levels (3–5) to avoid metal precipitation. Once the biomass and solutions are mixed, the flasks are closed and placed in an incubator at a specific temperature and rpm (for instance, 35°C and 50 rpm). Small liquid samples are taken from each flask at regular intervals to determine substrate or VFAs concentration. Sampling can stop when concentrations in all flasks remain constant for at least two consecutive points.
Once all measurements are done, toxicity is determined in terms of the half-inhibitory concentration, IC50, which is the concentration at which microbial activity is decreased by 50%. Microbial activity is determined by calculating the difference between the initial concentrations and final concentrations in each flask and dividing it by the concentration difference of the blank (Equation 3). IC50 is then determined graphically from a plot of “% activity” versus “metallic concentration”. The blank is considered to have a 100% microbial activity since no metallic inhibition takes place, but the activity decreases with increasing heavy metal concentration.
where
A(%): microbial activity.
D0, D48: concentration of substrate or VFAs at times 0, and t, respectively. Dc0, Dc48: concentration of substrate or VFAs in the blank flask at times 0, and 7, respectively.
Biosorption isotherms are plots of biosorption capacity versus metallic concentration at equilibrium. Isotherms can be adjusted to adsorption models to determine other parameters useful in the scaling up of biosorption processes, such as maximum biosorption capacity and affinity coefficients. To determine biosorption capacity, batch tests are carried out in a similar fashion to toxicity tests, but the variable of importance is the heavy metals concentration. A known amount of biomass (or immobilized biomass, if desirable) is put into a series of flasks and mixed with fixed volumes of metallic solutions. Metallic ions concentrations are determined by atomic absorption spectrometry. Biosorption equilibrium takes place when concentrations in all flasks remain constant for at least two consecutive points, and sampling can stop. Biosorption capacity can then be calculated according to Equation 4 [27].
where
q = biosorption capacity, (mg metal/g VSS);
C0 = initial metal concentration (mg metal/L);
Cf = final metal concentration (mg metal/L);
S = biosorbent (biomass) used (g);
V = volume of metallic solution (L).
The data at equilibrium (concentration and biosorption capacity) can be adjusted to established adsorption models. A correlation factor can be calculated by lineal regression to determine which model fits best to the experimental values. The most commonly used models in the literature are the Langmuir and Freundlich models.
Continuous studies can be carried out in bioreactors of all shapes and sizes, but the most commonly used configuration is the anaerobic packed bed reactor (APBR). Generally, wastewater flows upward through the reactor bed, and the use of a material support prevents from biomass losses and enhances bed stability. Environmental conditions depend upon the type of biomass used. Figure 3 shows the schematic diagram of an APBR used for the biosorption of heavy metals [28]. Bioreactors startup times are varied, and the parameters commonly measured during operation are pH, chemical oxygen demand (COD), substrate consumption, methane formation, VFAs formation, volatile suspended solids (VSS). Recirculation of the effluent can be added to the reactors configuration to enhance biomass growth before biosorption takes place.
The COD values are a measure of the organic load of wastewaters. When both the influent and effluent points are sampled, the COD analysis provides a quantifiable measurement of the removal efficiency of organic matter in the bioreactor. The most common COD method involves digestion of the sample at 120°C followed by a colorimetric analysis. The procedure is thoroughly described in the Standard Methods for the Examination of Water and Wastewater [26].
VFAs concentrations are indicative of the acidogenic activity of anaerobic biomass. Total VFAs can be analyzed by a simple titration method (using hydrochloric acid and sodium hydroxide) proposed by [29] Powell and Archer (1989). Specific VFAs, such as acetic acid, propionic acid, or butyric acid can be analyzed by HPLC. For the determination of substrate consumption, most methods are relatively simple and involve colorimetric techniques. A method utilized for glucose concentration is the DNS (3,5-dinitro-salicyclic acid) method, where the free sugar reduces the DNS reagent at high temperature, resulting in the formation of a colored product that absorbs light at 540 nm [30].
Example of an APBR used for heavy metals biosorption.
Once the startup stage is complete, heavy metals can be fed to the bioreactor to initiate the biosorption stage. A plot of C/C0 versus time is known as a rupture curve, where Co is the inlet concentration and C is the outlet concentration. Rupture curves provide information about the quality of a biosorbent in terms of the breakthrough time, saturation time, and retention capacities. The breakthrough time, tb, is defined as the time in which the outlet concentration is equal to a maximum permissible value (usually 10% of the inlet concentration or lower). Saturation time, ts, is the time in which the column is completely saturated by the metallic ions. Metallic retention capacity, Qads, can be calculated according to the following equation:
where
Qads: Retention capacity [mg/gVSS];
Cads: Co-C [mg/L];
t0: Initial time [d];
ts: Saturation time [d];
F: Volumetric flow [L/d].
Removal efficiency can also be determined simply by calculating the total metallic load and final metallic retention.
Bed characterization in anaerobic reactors is usually achieved by the following techniques: fraction of solids, Gram staining, microscopic observation via optical microscopy or scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS). These analyses supply plenty of information about the morphology and structure of the microorganisms and extracellular polymeric substances of the biofilm. XRD and EDS are especially helpful when a material support is utilized since these analyses provide the elemental composition of the different solid phases of the bioreactor bed.
The microbial sulfate-reducing process (SRP) has been utilized as a potential tool for heavy metals removal during the final steps of wastewater treatments and effluent recovery of several industries. Under anaerobic conditions, sulfate-reducing bacteria (SRB) reduce sulfate to sulfur, which reacts with the metallic ions, resulting in the formation of metallic sulfurs. Metallic sulfurs are universally identified because of their low solubility in aqueous systems, making the sulfate-reducing process an effective alternative for wastewater treatment [31]. Furthermore, selective recovery of economically important metals is also possible [32]. The sulfate-reducing process is successfully applied in the removal of metallic ions and sulfates in acid mine drainage (AMD), and can be useful in the removal of the remaining metals in industrial wastewaters [31].
During the SRP, sulfate ions
Formation of sulfur and alkalinity (sulfidogenic oxidation) is defined by Equation 6, where CH2O represents the electron donor:
When H2 is used as electron donor, the reaction generates hydroxide ions:
The formation of biogenic sulfur (H2S, HS-, S2-) enhances precipitation of dissolved metals, where M2+ represents metallic ions such as: Zn2+, Cu2+, Ni2+, Co2+, Fe2+, Hg2+, Pb2+, Cd2+, o Ag+:
The precipitation of metallic ions releases protons which acidify the water. Consequently, it is necessary to reduce the excess of sulfate to compensate acidity. The alkalinity of the hydroxide ions or bicarbonate produced during the sulfidogenic oxidation neutralizes the acidity of water.
The SRP is a valuable biotechnological tool for heavy metals removal in mining lixiviates and industrial effluents. It is considered potentially superior to other biological processes due to its capacity to produce alkalinity, neutralize the pH of acidic water, and simultaneously remove organic matter, sulfates and heavy metals [39, 32, 40, and 38]. Furthermore, recent studies of the SRP have revealed potential immobilization for metalloids (arsenic), radioactive isotopes (uranium), and cyanides [41, 42, and 43]. The SRP has also shown applications in organic matter removal and degradation of xenobiotic and toxic compounds [44].
The most commonly known advantages of the SRP are the low formation of metallic sulfur sludge (small volume and low solubility) compared to hydroxide precipitation and the recovery of economically important metals and precipitated metallic ions [45]. Recently, some methods have been implemented to selectively recover metals through pH and sulfur control [33].
It has been reported that metals are inhibitory agents for anaerobic microorganisms, including SRB [46, 47]. The inhibition is mostly due to the capacity of metals to deactivate enzymes by reacting with other sulfhydryl groups (-SH) and replacing the metals that constitute the active sites, such as Cu(II), Zn(II), Co(II), Ni(II). The deactivation of enzymes implies a negative impact on bacterial growth and activity [48]. There are some discrepancies in the literature with regard to the inhibitory levels of heavy metals over SRB because the majority of experiments are carried out at different environmental conditions [49].
Biogenic sulfur (produced during the SRP) forms complexes insoluble with heavy metals, resulting in the precipitation of metallic sulfur and, in turn, a toxicity reduction [46]. Sulfur inhibition may be decreased by precipitating sulfur with iron [50]. Several studies have focused on the use of SRP for the precipitation of metallic sulfurs within the same reactors where the sulfate-reducing activity takes place. However, this method might increase the inhibition of SRB [51].
To reduce inhibitory effects and increase pH in anaerobic reactors, a portion of the wastewater can be recycled and mixed with the influent. The remaining sulfur in the recirculating effluent reacts with heavy metals and causes precipitation of metallic ions before they get in contact with the anaerobic sludge [52]. The search of new strains tolerant to sulfurs or the special designs of bioreactors can help to prevent the toxic effect of heavy metals on SRB [53].
Another problem associated with heavy metal precipitation within the reactor is that metallic sulfurs are deposited on the biomass, and the contaminated sediments generate an increase in volume [54]. Moreover, contrary to general belief that only soluble metallic ions cause inhibition, it has been proven that metallic sulfurs affect the metabolic activity of SRB. Metallic sulfurs are not toxic, but they block substrate and nutrients access into the cells by forming a barrier on the cellular walls of SRB [47]. A proper alternative to separate the biological process from the precipitation is to use a two-step process, where metallic precipitation is isolated from the biological process [54].
Metallic sulfurs are generally highly insoluble at neutral pH, whereas some compounds, such as CuS, are insoluble at pH values as low as 2. The great advantage of precipitation is the possibility for selective recovery of metallic sulfurs. It has been shown that each metal precipitates at a unique sulfur concentration S2–, or potential (pS), directly related to the solubility of the metallic sulfur formed. Controlling these concentrations within a precipitator can be carried out using pH electrodes and sulfide ion selective electrodes (pS electrode). The unique quality of the potential level (pS) of each metal has been successfully applied as a controlling parameter for the selective precipitation of metals and formation of pure metallic sulfurs suitable for reutilization. The success of the precipitation process depends not only on the heavy metal removal from the soluble phase but also on its separation from the liquid phase. Thus, solid–liquid separation processes (for instance, sedimentation and filtration) are of great importance for a successful removal [55].
Biomass is retained within bioreactors according to the adherence properties of cells. Thus, bioreactors can be classified into two groups [56]: fluidized bed reactors and fixed bed reactors. In a fixed bed reactor, biomass is retained either by the formation of biofilms on static or suspended inert materials or by the obstruction of biological particles on packing materials (Figure 4). A biofilm is defined as a complex structure constituted by cells and extracellular products in elongated or granular forms [57]. In fluidized bed reactors (or free bed reactors), biomass is retained by forming biological particles of high density and sedimentability: granules. Methanogenic granular sludge and sulfate-reducing sludge are composed of microbial aggregates that grow by mutual bonding of bacterial cells in the absence of a support material [58].
Anaerobic reactors used in sulfate-reducing applications.
Numerous literature studies have applied multiple reactors designs of the sulfate-reducing process for the treatment of water with high concentrations of sulfates and heavy metals. Some of these designs include batch reactors (BR), sequencing batch reactors (SBR), continuously stirred tank reactors (CSTR), anaerobic contact processes (ACP), anaerobic baffled reactors (ABR), anaerobic filter reactors (AFR), fluidized bed reactors (FBR), gas lift reactors (GLR), anaerobic hybrid reactors (AHR), membrane bioreactors (MBR), and upflow anaerobic sludge blanket reactors (UASB) [38].
The kinetic model of a microbial process is defined as: the verbal or mathematical correlation between velocities and concentrations of reagents products, inserted into mass balances for the prediction of substrate conversion level and individual yields at specific operating conditions [59].
The complexity of the kinetic models used to describe the changes within the cell during a microbial transformation can be very broad. Several kinetic models proposed in the literature are summarized in Table 1 [60].
\n\t\t\t\tKinetic model\n\t\t\t | \n\t\t\t\n\t\t\t\tDefinition\n\t\t\t | \n\t\t
Unstructured – non segregated | \n\t\t\tBiomass is considered the only component. An average cell is representative of the microbial consortium. | \n\t\t
Metabolic | \n\t\t\tMetabolic pathways are described as a network of reactions using a simplified reaction scheme. Stoichiometric relations are defined. | \n\t\t
Sructured (or Cell) | \n\t\t\tBiomass is considered to be constituted by several species. Intracellular components are taken into account. | \n\t\t
Segregated | \n\t\t\tThe distribution of a property is considered in the description of the biomass. | \n\t\t
Classification of kinetic models.
The simplest models, unstructured-non segregated models, have been used for numerous engineering troubleshooting applications. However, in order to have a better system description it is necessary to use models that take into account complex reaction schemes, i.e., models that take into account the metabolic pathways of each microorganism.
A mathematical model is the abstract representation of a specific aspect of reality. Its structure is composed of two parts. The first part corresponds to all those characteristic aspects of an idealized reality, and the second part refers simply to the existing relationships between the aforementioned elements [61].
The order of reaction is an experimental magnitude dependent of the way in which velocity relates to concentration [62]. Any typical reaction in nature will occur at a rate dependent of certain factors, the reaction rate is indicated by a constant value (k). It is found that reaction rates are related to the reaction order according to the following mathematical expression [63]:
where
n = reaction order;
k = rate constant;
A = concentration of component A;
t = time.
This equation is integrated for every order of reaction (zero order, first order, second order, pseudo first order, and pseudo second order) as follows:
Zero order reaction:
Differential equation:
Separating variables:
Solving the integral:
First-order reaction:
Differential equation:
Separating variables:
Solving the integral:
Second-order reaction:
Differential equation:
Separating variables:
Solving the integral:
where C: Integration constant
Pseudo first-order reaction:
Differential equation:
Solving the equation:
Pseudo second-order reaction:
Solving the equation:
where
CA = amount of metal adsorbed (mg/L)
CAo = initial concentration (mg/L)
t = time (min)
k = equation constant (mg/L-min)
b = initial concentration of component b, constant throughout the reaction time.
If the lineal model properly fits the experimental values (i.e., a correlation factor, R2, close to 1) the adsorption process can be described as chemisorption [64].
The development of biosorption systems is dependent of many factors including: temperature, pH, biosorption capacities and selectivities, recovery efficiency, and resistance to other components or operating conditions. Nevertheless, most biosorption studies focus on the measurement of the biosorption capacities of biomass [65]. The quantification of the sorbate–biosorbent interactions is fundamental for the evaluation of the biosorption capacity. Due to the similarity between the biosorption process and the adsorption process, biosorption capacity can be analyzed by sorption isotherms. Sorption isotherms are model equations that represent the behavior of experimental data.
The Langmuir and Freundlich equations are two of the most utilized adsorption models. These models are described by the following equations [1]:
where
qe: biosorption capacity at equlibrium (mg/g VSS).
qmax: máximum biosorption capacity (mg/g VSS).
Ce: metallic concentration at equilibrium (mg/L).
b: affinity coefficient between the sorbate and the biosorbent (L/mg).
qe and Ce are obtained at the equilibrium point, whereas qmax and b can be determined graphically by a plot of (1/qe) versus (1/Ce).
where
qe: biosorption capacity at equilibrium;
Ce: metallic concentration at equilibrium;
k,n: Freundlich constants.
The parameters k and n can be graphically determined from a plot of Ln(qe) versus Ln(Ce).
Environmental pollution is one of the main problems of our society. Heavy metals constitute a major group of contaminants characterized by having a density five times greater than that of water. One of the main sources of heavy metals pollution is the acid mine drainage (AMD) generated by mining industries. The AMD is an acid lixiviate that may contain high concentrations of sulfates, iron, calcium, zinc, manganese, aluminum, copper, and other types of toxic elements such as arsenic and lead. In México, several regions have been affected due to the presence of heavy metals in wastewaters, which generates the necessity of implementing economic and efficient remediation techniques. The review focuses on biological methods and the advantages they offer over conventional treatments. One particular alternative studied in recent years is biosorption – based on the ability of biomass to bind and concentrate heavy metals – because of its economic nature and high removal efficiencies in dilute wastewaters.
Biological technologies provide plenty of advantages and can be just as effective and economic as other technologies (Table 2). However, it is of upmost importance to continue with scientific research to acquire an improved understanding of the bioremediation processes and optimize industrial applications.
\n\t\t\t\tMethod\n\t\t\t | \n\t\t\t\n\t\t\t\tAdvantages\n\t\t\t | \n\t\t\t\n\t\t\t\tDisadvantages\n\t\t\t | \n\t\t
Chemical precipitation | \n\t\t\t-Low cost -Simple operation | \n\t\t\t-Excessive formation of sludge -Slow or insufficient precipitation | \n\t\t
Reduction | \n\t\t\t-No residual sludge generation | \n\t\t\t-Formation of toxic gaseous products -Difficult handling of reagents | \n\t\t
Ultrafiltration | \n\t\t\t-Requires small operating space -High separation | \n\t\t\t-High operating costs -Membrane clogging | \n\t\t
Reverse osmosis | \n\t\t\t-High efficiency -Removal of other ionic compounds | \n\t\t\t-High operating and maintenance costs | \n\t\t
Electrodialysis | \n\t\t\t-High efficiency | \n\t\t\t- High energy requirements (high pressures) -Clogging | \n\t\t
Adsorption-Ionic Exchange | \n\t\t\t-Simple operation -High adsorption capacity | \n\t\t\t-Low selectivities - pH sensitive | \n\t\t
Phytoremediation | \n\t\t\t-Eco-friendly technology and low cost -Removes other types of pollutants | \n\t\t\t-Depends on the growing conditions. Long remediation times. It requires expensive agricultural equipment. | \n\t\t
Microbial bioremediation | \n\t\t\t-Eco-friendly technology and low cost -Generates no toxic waste (CO2, H2O) | \n\t\t\t-Limited pollutants range -Microbes need proper growing conditions. | \n\t\t
Biosorption | \n\t\t\t-Low cost -Minimum formation of residual sludge -Potential for metal recovery -Simple operation -Effective in diluted solutions. | \n\t\t\t-Toxic effects on living biomass. -Constant nutrient supply for biomass growth. | \n\t\t
A comparison between the existing methods for heavy metals removal.
The authors would like to thank the Department of Chemical Engineering and Metallurgy of the University of Sonora for the space and equipment facilitated during the realization of this work.
Land is shrinking but world population is increasing in a rapid phase, so, modern agricultural practice is struggling to meet the level of primary productivity required to feed approximately 10 billion people by 2050 [1]. From last few decades the adverse effects of climate change and higher CO2 concentrations, the consequence of expected impacts on the water-use efficiency of dryland as well as irrigated crop production, potential effects on biosecurity, production, and quality of product through increased the frequency of introduced various abiotic (heat, salinity and drought) and biotic stresses (pests and diseases). In addition, climate change is also expected to cause losses of biodiversity, mainly in more marginal environments. Drought alone is expected to reduce crop productivity in half of the global arable land and it’s estimated around 50% in the next five decades [2]. It has been predicted that, on average, global yields of major economic important crops will be reduced by the unfavorable climatic conditions in wheat (6.0%), rice (3.2%), maize (7.4%) and soybean (3.1%) for every degree celsius increase in global mean temperature [3].
Climate resilience is an ability of the plant/crop to survive and recover from the effects of climate change. Some important practices that may help to adapt the climate change are soil organic carbon build up or carbon sequestration, in-situ moisture conservation, residue incorporation instead of burning, water harvesting and recycling for supplemental irrigation, growing biotic and abiotic resistance/tolerant varieties, location specific agronomic and nutrient management and breeding for multiple traits of interest including quality.
Plant Breeding has always played a pivotal role in human history from revolutionizing agriculture to feed the ever-growing population. The key role of plant breeding in agriculture is to develop a genetically superior genotype/variety, which is suitable for a specific as well as general cultivation of particular environment towards higher production [4]. Realizing the importance of genomic resources to expedite the breeding programs, huge amount of genetic data related to genes and QTLs (Quantitative Trait Loci) are generated after the advent of molecular biology and biotechnology [5]. The progress in precise phenotyping and genotyping offers tremendous opportunities to develop crop varieties that are suit for better changing the climatic conditions, which ameliorate in boosting the plant breeding activities for developing climate resilient varieties/cultivars [6]. Hence, development of climate resilient varieties utilizing Smart breeding tools to ensures the food security in adverse climatic conditions.
The effect of climate on agriculture is related to variability’s in local climates rather than in global climate patterns. The changes in the rainfall patterns, temperature, CO2 level and greenhouse gases resulting in the frequency and severity of extreme events such as flooding, drought, hail, and hurricanes etc. are major hindrance in achieving the food security for ever increasing population [7].
According to Intergovernmental Panel on Climate Change (IPCC), global temperature may be rise from 1.7 to 4.8°C during the twenty-first century and precipitation pattern will also be altered [8]. In recent times, it has been reported that the Yangtze river basin in China has become hotter and it is expected that the temperature will increase up to 2°C by 2050 relative to 1950 [9], and also reduce the rice (41%) and maize (50%) production by the end of the 21st century. This shift in climate will affect the environment, including the soil ecology and thus has the potential to threaten food security through its adverse effects on soil properties and processes [10]. Additionally, the direct and indirect effects of climatic change would lead to alter the nutrient and their bioavailability in soils (Figure 1). The effect of climate changes on biotic and abiotic stresses have already reduced the global agricultural production from 1 to 5% during the past three decades [11].
Adverse effects of climate change on agriculture and food production.
Some important practices that assist to adapt the climate changes for crop production including (i) Building resilience in soil (tillage management, avoid bare soil, fertilizer application after mandatory soil testing, increase soil carbon through organic manure, green manuring, crop rotation or intercropping with legume sequester carbon and biochar), (ii) Adapted cultivars and cropping systems (crop diversification, shallow-deep root and legume-cereal cropping system, improved early/short duration cultivars for tolerant against drought, heat and submergence capturing optimum yields despite climatic stresses), (iii) Rainwater harvesting and recycling (inter-row water harvesting, inter-plot water harvesting, in farm ponds and reservoirs and recycling), (iv) Farm machinery (chisel and para plow to opening the furrows which conserves rain water, laser leveler helps in increasing nutrient as well as water use efficiency), (v) Crop contingency plans (livestock and fishery interventions), (vi) Weather based agro advisories (automatic weather stations establishment at experimental farms and mini-weather observatories records for real time weather parameters such as rainfall, temperature and wind speed, which customized through agro advisories and improve weather literacy among the farmers).
Plant breeding procedures have been constantly evolving to meet the increasing food demand. The art of plant breeding has been practiced in various forms since the start of human civilization. In conventional plant breeding, development of a new cultivar take around 10–14 years and may even exceed this period based on the plant habit, reproductive cycle and complexity of traits involved. The rapid climate change necessitates the development of varieties in a shorter period to tackle with the unpredictable weather parameters. The concept of Smart breeding is an integration of conventional breeding strategies with advanced molecular, genomic and phenomic tools to efficiently and effectively breed the resilient crop cultivars with enhanced yield potential. New breeding approaches such as rapid generation advancement, doubled haploid (DH), marker assisted back crossing (MABC), marker assisted recurrent selection (MARS), genomic selection (GS) etc. have been used to help shorten the breeding cycle along with efficient screening for specific biotic and abiotic stresses. Biotechnology-based breeding technologies (marker-assisted breeding and genetic modifications) will be essential to assist and accelerate genetic gain, but their application requires additional investment in the understanding, genetic characterization and phenotyping for complex adaptive traits to be exploited for climate resilient breeding.
Climate change leading to severe weather fluctuations would also lead to evolution of plant diseases and pests, exposing crops to higher biotic pressure in addition to abiotic stresses. To make crop adaptation feasible in the era of changing climate, there is indispensible need to breed the crop plants with diverse genetic backgrounds. In order to feed the mushrooming population, there is urgent need to use crop wild relatives for developing broader spectrum varieties to tackle various biotic and abiotic stresses. During the era of domestication, selection preferences lead to modern crops with narrow genetic background, resulting in limitation of environmental adaptation and breeding capacity using modern germplasm [12]. Wild relatives and ancestral species relatively possess broader adaptation to environment and climates ultimately higher potential in crop improvement.
Prebreeding activity is a bridge for linking the desirable traits of CWR to the modern cultivar development by providing breeders with wild genetic diversity in a more immediately usable form [13, 14]. Pre-breeding is an opportunity to introgression of desirable genes, from wild species (primary, secondary and tertiary gene pools) into elite breeding lines/cultivars/genotypes, to overcome the linkage drag (Figure 2). Almost all cultivated crop species were originally domesticated from wild plants by humans, due to domestication inherently reduced the genetic variation [15]. The genetic potential of wild relatives has been reported in different crops like rice, wheat, maize, potato, tomato, cotton, tobacco, sugarcane, chickpea and pigeonpea [16, 17, 18, 19, 20, 21].Genomics strategies have been widely utilized in staple crops for transferring major genes (i.e. disease resistance) from wild germplasm to elite cultivars [22]. It is well documented that application of molecular mapping and sequencing to could be useful to unlock the genetic potential of CWR [23]. So, crop wild relatives (CWRs) are good reservoir of untapped genetic diversity, which may not exist in the cultivated gene pool that can be used to improve the numerous trait of interest including resistance/tolerance against diseases, insect-pests, drought, salinity, cold, heat and good agronomic adaption with quality improvement.
Untapped genetic resources/ CWRs towards the germplasm enhancement.
Wild species are used mainly for the introgression of disease and insect resistance into crops although drought, cold, heat and salinity tolerance have also been addressed in some staple crops. This is because most pathogens have faster adaptation to climate rendering cultivars vulnerable to novel deadly diseases [24]. The use of interspecific or intergeneric hybridization for disease resistance introgression is conventional one. Another potential technique to enhance genetic diversity and facilitate crop vigor with adaptation to different environmental niches is creating the polyploidy crops mimicking natural evolution through hybridization [25]. Enriched genes for biotic and abiotic stress resistance of CWR can be studied using comparative pool sequencing of genome assemblies, elucidating the potential genomic segments responsible for adaptation to different ecological niches. These have been explored in wild relatives of many crops including chickpea, barley and maize [26, 27, 28, 29].
To address the diversity within species, pan-genomics based on entire gene repository of a species can reveal the genetic variations such as structure variants (SVs) and single nucleotide polymorphism (SNPs) abundantly found in plants. One such example under SVs is presence/absence variants (PAVs) of Elicitin response (ELR) gene between wild and cultivated potato leads to resistance/susceptibility response to late blight disease [30]. Larger pan-genomes including both wild relatives and cultivars can acquire glut 0f dispensable genes resulting in phenotypic variations; thereby easing out with characterization of the trait associated genomic variants [31]. To tackle the deadly rust diseases in wheat in the context of changing climate, several pan-genomic R genes have been successfully identified and cloned from wild diploid wheat Aegilops tauschii [32].
Considering the risks of introducing foreign alleles into cultivars, other potential technique for developing climate-friendly crops is de novo domestication [33]. As most staple crops are grown majorly in the regions other than where they were originally domesticated with different climatic regimes. Nevertheless, their wild relatives and landraces exhibit better adaptation to local climate in the native regions. In the scenario of climatic change, there is chance to leverage this opportunity to use those underutilized or orphan crops e.g. rise in Sinapis alba (white mustard) acreage replacing the B. napus in Europe for biofuel production [34]. A pipeline strategy has been proposed for domestication of wild germplasm in some orphan crops such as quinoa [35]. In addition to direct planting of non-domesticated crop plants, relatively advance methodology of CRISPR/Cas9 boosts the wild germplasm domestication by editing of domesticated genes e.g. editing in wild tomatoes (Solanum pimpinellifolium) and ground cherry (Physalis pruinosa) mainly focused on flower improvement, plant architecture improvement, fruit size, fruit number and nutritional content [36, 37, 38]. It is evident from such a few successful introgressions of domesticated genes that use of wild germplasm in regular plant breeding is quite promising in countering the effects of climate change on agriculture and hence, food security.
The actual potential of the CWR in plant breeding largely remains underexploited due to linkage drag and frequent breeding barriers with the crops. Introgressiomics approach allows mass scale development of plant material and populations with introgression lines from CWR into the genetic background of crops [39]. This pre-emptive breeding technique could be focused or unfocused depending upon the objective. Besides genetic analysis of traits present in CWR, MAS driven generation of chromosome substitution lines (CSL), introgression lines (IL) or MAGIC populations allow the development of genetically characterized elite material. Genomic tools like high throughput molecular markers facilitate the characterization and development of Introgressiomics populations, which can be easily incorporated into major breeding programs for coping with the accelerating environmental challenges.
After the introgression into domesticated background from CWR, populations such as backcross populations (BC), recombinant inbred lines (RILs), doubled haploids (DH), near isogenic lines (NILs), multiparent advance generation intercross (MAGIC) populations as well as nested association mapping (NAM) populations are developed to study the introgressed gene(s). After mapping their locations on to the genome and it genotypic validation with molecular markers, they are further deployed using Marker assisted selection (MAS). Systematic screening of the huge number of progenies with MAS enhances the efficiency of breeding program (van de Weil 2010). Desirable recombinants can be developed at early generations using larger populations e.g. using marker-assisted backcrossing (MABC), an important QTL was introduced into a new lowland rice background in just 2 rounds of backcrossing [40].
Genomic scans can also reveal candidate domestication and improvement loci as well as post-domestication introgression using CWR [41, 42] to be further harnessed in the scenario of climatic challenges. In case of CWR, high throughput sequencing offers a cheap and rapid way to deploy thousands to millions of markers for mapping purposes [43]. Reduced representation techniques as genotyping by sequencing (GBS) or even nimble exom capture have been exploited to this effect in several CWR species already [42, 44, 45]. These technologies offer rapid marker density as required for rapid fine mapping and can saturate mapping populations in terms of capturing all of the recombinants.
The availability of a reference genome sequence in CWR during recent times greatly boosts the use of high-throughput sequence data. Some large scale genomic sequencing and re-sequencing programs are well underway [27, 46] often with reduced representation methods. Whole genome shotgun sequencing (WGS) techniques can also be utilized to characterize CWR germplasm for climate resilience breeding in major staple crops. E.g. Rice having smaller genome size (430 mb) long with its wild relatives has been re-sequenced using WGS [47, 48, 49]. Already sequenced germplasm collections including Chickpea [50], Rice [48], Soybean [51] and Wheat [52] etc. will provide insights into these diverse gene pools to be exploited in combating various biotic and abiotic challenges during this era of climate change. More recently, a massive scale genomic study of almost 80000 accessions from CIMMYT and ICARDA unraveled unprecedented amount of genetic diversity in 29 wheat species comprising cultivated wheats, CWRs and landraces to be exploited in wheat improvement for range of climate related plant traits [53].
Potentially revolutionary technology in modern plant breeding like genome editing has enabled scientists to alter genome of any organism with unprecedented precision without involvement of any foreign DNA [54]. CWR and their sequence information may serve as a reference library for all kind of diversity. This information on allelic diversity and its phenotype is a vital requirement for many genome editing approaches. In fact, these approaches will allow the use of this information from more distantly related, cross-incompatible CWR and domesticated species to be further utilized in crop improvement [55, 56].
Considering the various direct and indirect impacts of climate change on food production and agriculture along with rapid deterioration of arable land and perplexity of rainfall patterns, all these factors triggering various abiotic stresses such as drought, heat stress and biotic stresses like pest and disease attacks, the sophisticated techniques laden biotechnology toolkit has potential to address these immense challenges of developing the stress tolerant food crop cultivars in this hour of need [57]. With population growing at rapid rate under threatening scenario of climate change, it is high time to shift resilience from conventional breeding along with fertilizers and pesticides to genomics-assisted crop improvement techniques in order to achieve more sustainable and efficient yield gains [58].
Recent advances in biotechnology tools have the potential to understand the function of genes/QTLs that govern the economic traits, and applying this information’s to Smart breeding programs, leading to crop improvement. The advent of molecular markers such as Restriction fragment length polymorphism (RFLP), Rapid Amplified Polymorphic DNA (RAPD), Simple Sequence repeat (SSR), Kompetitive allele specific PCR (KASP), Cleaved amplified polymorphic sequence (CAPS) and especially Single Nucleotide polymorphism (SNP) have revolutionerized the field of plant genetics and facilitated molecular crop breeding [59].
The ultimate goal of crop breeding to develop super-varieties by assembling multiple desirable traits, such as yield related, superior quality, tolerance/resistance against biotic and abiotic stress and good environmental adaption. It is very challenging, difficult and time consuming to combine all traits in single genotypes by traditional breeding, so some alternates need to be compiling all important traits, into single varieties, can be done through marker assisted selection (MAS), which have become an integral component of genotypes/germplasm improvement. The potential benefits of using molecular markers linked to the genes/QTLs of interest in breeding programmes, which have shifted from phenotype-based (traditional breeding) to a combination of phenotype and genotype-based selection, are of great importance to the Smart breeding programme [60].
Breeding programme combine, with MAS strategies have major advantages compared to traditional phenotype-dependent breeding in terms of convenience and efficiency for transferring the genes/QTLs of interest to the plant genome [61]. Selection can be done selectively with the genotypes of molecular markers linked to the target traits, selection in off-season nurseries (reduce breeding cycle), making the technique more cost effective to grow for more generations per year (speed breeding), reduction of required population size because many lines can be discarded in earlier breeding generations after MAS. The most effective and usefulness of MAS approaches, for traits of simple inheritance (qualitative traits controlled by one or a few genes) have been well proven in many important crops [62].
Basically, two major MAS strategies are usually applied in breeding programme, (i) backcrossing for favorable alleles into elite germplasm, i.e. marker-assisted-backcrossing (MABC) and (ii) stacking multiple genes of different sources into elite breeding lines, i.e. marker-assisted gene pyramiding (MAGP). The success of MAS has depends to search the important QTLs for complex traits (controlled by minor genes), which account for a large proportion of phenotypic variation (major QTLs). Successful applications of MABC and MAGP for improving yield or yield component traits by using well characterized major QTLs/genes in important crops [63]. Successful implementation of MAS breeding in broad range of crops including barley, beans, cassava, chickpea, cowpea, groundnut, maize, potato, rice, sorghum, and wheat [64]. Genetic markers associated with agronomic traits can be introgressed into elite crop genetic backgrounds via marker assisted breeding (MAB). It allows stacking of desirable traits into elite varieties to make them better adapted to climatic changes.
With plummeting cost and greater accessibility of high throughput genome sequencing technology, the breadth of genomic data is expanding rapidly. In order to capture diversity of specific gene families within a large group, DNA samples can preferentially be enriched before sequencing. This approach can be adopted to define genetic variation in disease resistance gene repositories in Solanaceae and Triticeae (RNA seq) [65] and gluten gene families I bread wheat (GlutEn Seq) [66].
Sanger sequencing to study plant genomes is unfeasible due to low throughput and high sequencing costs. In 2005, Roche released its revolutionary 454 pyrosequencing platform [67]. Subsequently, several sequencing platforms such as developed by Illumina, ABI, Life technologies, PacBio, Oxford Nanopore and Complete genomics were released commercially, changing the scenario of genome sequencing. Depending on chemistry, second generation sequencing (SGS) approaches are classified as ligation based approaches and synthesis based approaches [68]. To rectify the problems of assembling repetitive genomic regions, long read sequencing offers solution by producing reads spanning the repeat regions [69].
Rapid cost reduction in genome wide genotyping allows large scale assessment of crop species diversity to capture climate related traits. It leverages cheaper sequencing to identify up to millions of SNPs in plant population [70]. High SNP density approach like whole genome resequencing (WGR) & low SNP density approach like reduced representation sequencing (RRS) are majorly used approaches. However, high density genotyping assay “SNP chips” enable large scale genotyping using SNP specific oligonucleotide probes rather than direct sequencing.
The variants identified by genotyping by sequencing (GBS) can be used for conventional QTL analysis and modern approach like genome wide association studies (GWAS). GWAS exploits the past recombinations in a diverse association panels to identity genes lined to phenotypic traits [71]. SNP genotyping have been widely used in many crops including wheat [72] and Maize [73]. Extensive use of GWAS is resulting in our enhanced understanding of genetics of important climate specific traits viz. drought and heat tolerance. In light of reducing sequencing cost and expensive validation of candidate genes, use of WGR to further enhance resolution of mapping studies is likely to become routine task in future [70].
The availability of reference genome assembly rewards us with information about gene content, ability to associate the traits with specific genes with subsequent insights into related biophysical and biochemical roles of gene(s) in the expression of that particular trait [74]. Resequencing of diverse crop cultivars reveals the gene content variation and DNA sequence differences between allelic variants, while sequencing of expressed gene products provides information on where and when genes are functioning. Such information when integrated within breeding pipelines, offers promise to accelerate the development of climate smart crop varieties.
The recent explosion in genomic data is rapidly triggering a fundamental shift to genomic based breeding [75]. The ability to identify and genotype umpteen SNPs at ever reducing costs facilitated expansion of MAS in breeding to plethora of traits and across wider range of crops [76]. A major outcome of availability of high throughput genome wide markers is a move towards population based trait association and breeding i.e. NAM or MAGIC populations to ultimately enhance the trait mapping resolution by greatly increasing the number of recombinations in the population. After identification and validation of the candidate genes, there achieved the deeper understanding of biological mechanism underlying the trait, which can subsequently be improved through MAB or genetic alterations. Furthermore, precise understanding of the molecular basis of traits enables the engineering of novel alleles or mining of potentially desirable alleles from CWR, facilitating further enhancement of the trait.
Genome editing has enabled breeders to precisely add or delete any DNA sequence in the genome and has shown enormous potential to revolutionize the crop improvement in this very decade [70, 77]. Some approaches like transcription activator-like effector nucleases (TALENs) and zinc finger nucleases (ZFNs) have been in the game for more than 2 decades. However, type II clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (Cas) system from Streptococcus pyogenes [78] developed in last decade has been most versatile tool in breeder’s toolkit to introduce desirable or novel traits and accelerate development of climate smart crop varieties.
Usually, a custom-made guide RNA (gRNA) along with Cas9 nuclease is delivered into plant protoplast, where Cas9 produces double strand break (DSB) 3 bp upstream of the NGG motif (protospacer adjacent motif-PAM sequence) [78]. Cellular repair machinery through non-homologous end joining (NHEJ) can lead to frameshift mutation causing a knock-out. Otherwise, a donor DNA template can be provided for precise genetic knock-in through homologous recombination (HR). CRISPR/Cas9 was initially used to disrupt genes related to disease susceptibility in crops such as OsERF922 gene disruption in rice for blast resistance [79] and loss of function in susceptibility gene TaMLO for powdery mildew resistance in wheat [80]. Genome editing has also been used to tackle some abiotic stresses in staple crops like a promoter of a gene AGROS8 was replaced with a stronger one to impart drought tolerance in maize [81].
Due to changing climates, it may be quite beneficial for the farmers to have early maturing varieties, which enables plants to complete crucial developmental periods before the onset of a stress. It has been achieved by disrupting a flower repressing gene SP5G to develop early maturing tomato varieties [82]. For instance, developing climate rice to grow in diverse climates, generally desirable traits are cold, heat and drought tolerance at seedling and reproductive stages [83]. Secondary characters like root and flag leaf traits can be useful to generate cultivars with improved drought and heat tolerance [84]. Here, CRISPR tools could prove to be of great value for exploration of the candidate genes from CWR (O. officinalis, O. nivara and O. glaberrima) for abiotic stress resistance [85].
Genome editing has also huge potential to accelerate the domestication of novel crops form CWR or minor crops with valuable traits for coping with extreme climatic events. This would allow the editing of key genes for domestication in potential new crops for rapid enhancement of currently limited gene pools to maximize the use of germplasm adapted to climate change. Also, multiplexing of CRISPR systems for simultaneous editing of multiple genetic loci can boost the speed and efficiency manifolds. However, there are a number of shortcomings in this approach including off target effects [86], low efficiency of HR, restrictive PAM sequences and regulatory concerns, which paved the way for advent of more sophisticated technologies like DNA free genome editing, base editing and prime editing.
Conventional genome editing using recombinant DNA (rDNA) leads to random host genome integration and can generate undesirable genetic changes or DNA damage [87], along with concerns over genetically modified organism (GMO) regulations with introduction of foreign DNA [88]. DFGE takes care of such critical issues along with reduced risk of off-targets. Initially, it was successfully deployed in rice and tobacco with transfection of protoplast with CRISPR-Cas9 ribonucleoprotein (RNP) [89]. Also, a particle bombardment mediated DFGE approach has been developed in wheat and maize [90, 91].
It is evident that a single base change can cause variation in the elite traits [92], so there required an efficient technique to cause precise and efficient point mutations in plants. CRISPR-Cas9 driven base editing is new approach which accurately transform one DNA base to another without repair template [93]. E.g. Cytidine deaminases convert cytosine (C) to uracil (U), which is treated as thymine (T) in subsequent DNA repair and replication, thus creating C•G to T•A substitution. It has been utilized in wheat, maize and tomato [94] and can be quite useful for gene functional analysis and therefore can assist breeding for better stress adapted varieties.
Another latest milestone in this genome engineering era called prime editing allows introduction of all known 12 base to base conversions in addition to mutations such as insertions and deletions using prime editing guide RNA (pegRNA) [95]. This promising approach opening up numerous possibilities for effectively targeting and modifying desirable genome sequences to accelerate functional genomics and introduction of genes for adaptation to diverse climates can boost breeding for climate smart crop varieties in near future [96].
In this rejuvenated plant mutagenesis breeding era, genome editing can be used in functional genomics for the identification of candidate genes for climate related agronomic, physiological and phonological traits, which can be exploited for crop improvement in adaptation to changing climate. Despite having enormous potential and real world applications of genome editing technologies, the regulatory and ethical concerns may limit it, as happened in a few European countries. In the nutshell, genome editing in complementation with conventional plant breeding can be adopted to develop and deploy climate smart crop varieties in the farmers’ fields.
Advances in phenomics and genomics have generated unprecedented amount of new data, enabling breeders to continuously pushing the crop yields on positive side [97]. Despite success in techniques like genomic selection (GS) in cereals and legumes, lack of predictive accuracy for many complex traits (yield) have revealed their inability to adequately model all relevant factors inherent to such traits due to complexity of the interactions between genetic and environmental components of phenotypic variation [98]. Several mapping studies have shown that such complex traits are controlled by minor genes (polygenes) with small but cumulative effect, hence go undetected while analyzing them in smaller population size.
Relationship between genotype and phenotype is not always linear and small changes on one hierarchical level may have bigger impact on other levels. Many statistical models therefore fail to accurately delineate the non-linear relationships. Additionally, epistatic interactions are hard to detect while mapping genotype to phenotype with linear models due to low power and sheer computational demand [99]. With continuously falling cost of genome sequencing, advent of innovative genetic assays to explore missing heritability and genetic regulation, breeders have access to wide range of high-throughput sensors and imaging techniques for spectrum of traits and field conditions.
Omics technologies (genomics, transcriptomics, proteomics, metabolomics, phenomics, epigenomics and microbiomics) together with approaches to gather information about climate and field environment conditions have become routine in breeding programs now a days. However, ability to accurately predict & select best lines for the specific environment relies on our ability to model these immensely complex systems from web of genomic and phenomic data at hand e.g. multiomics big data. Integrating with phenomics and genomics, AI technologies by assisting with big data, can boost up the development of climate resilient crop varieties with enhanced yield potential and stability and improved tolerance to expected simultaneous environmental stresses (abiotic and biotic).
Accelerated plant breeding for climate resilience is critically dependent upon high resolution, high throughput, field level phenotyping that can effectively screen among better performing breeding lines within larger population across multiple environments [100]. With advent of novel sensors (unmanned air vehicle-UAV), high resolution imagery and new platforms for wide range of traits and conditions, phenomics has been elevating the collection of more phenotypic data over the past decade [101, 102]. High throughput phenotyping (HTP) allows the screening for plant architectural traits and early detection of desirable genotypes. It enables accurate, automated and repeatable measurements for agronomic traits (seedling vigor, flowering time, flower counts, biomass and grain yield, height and leaf erectness, canopy structure) as well as physiological traits (photosynthesis, disease and stress tolerance). HTP methods such as RGB imaging, 3-D scanning, thermal and hyper spectral sensing and fluorescence imaging have been successfully utilized to identify, quantify and monitor plant diseases [103].
By coupling GWAS with high throughput phenotyping facilities, phenomics can be adopted as novel tool for studying plant genetics and genomic characterization enhancing the crop breeding efficiency in era of climate change [104]. Recently, deep learning (DL) has been extensively used to analyze and interpret more phenomic big data, especially for advancing plant image analysis and environmental stress phenotyping [105].
Genomic selection as been extensively used breeding approach for climate resilience in agriculture in last decade, especially for complex polygenic traits. It involves prediction models developed by estimating the combined effect of all existing markers simultaneously on a desirable phenotype. Highly accurate prediction can result into enhanced levels of yields by shortening the breeding cycles. Omics layers (gene expression, metabolite concentration and epistatic signals) can be better predictors of phenotype than SNPs alone due to their molecular proximity to the phenotype. Many such omics layers that explain trait variation have not been made available to the statistical models lowering down its efficacy. Several approaches such as mixed effect linear models and Bayesian models to select only most important predictive SNPs are majorly used.
From the prospective of breeding, by accessing the rich set of omics and environmental data lying between plant genotype and its phenotype, superior and refined impact can be achieved on desirable phenotype. Next gen AI holds promise for GS as acquisition of large scale genomics and phenomics data in addition to molecular layers between them such as transcriptomics, proteomics and epigenomics will facilitate a period, where AI models can identify and explain the complex biological interactions [99].
Next gen AI will surely require knowledge and rationality of breeders as well as farmers to evaluate the efficacy of outcomes. In coming times, agriculture will rely on Next Gen AI methods for making decisions and recommendations from big data (highly heterogeneous and complex) that are representative of environment and system biology based understanding of the behavioral response of plants.
The current pace of yield increase in staple crops like wheat, rice and maize is insufficient to meet the future demand in the wake of climate change [106]. A major limiting factor in plant breeding is the longer generation times of the crops, typically allowing 1–2 generations in a year. Several ‘speeding breeding’ protocols, using extended photoperiods and controlled temperatures have enabled breeders to harvest up to 6 generations per year by reducing the generation time by more than half [107]. Such protocols have been reported in several important crops such as spring wheat (Triticum aestivum) [108], barley (Hordeum vulgare) [109], chickpea (Cicer arietinum), rice (Oryza sativa) [110] and canola (Brassica napus).
Speed breeding can potentially accelerate the discovery and use of allelic diversity in landraces as well as in CWR to be further used in developing climate resilient crop varieties. One such example is recent discovery of new sources of leaf rust resistance after screening of the Vavilov wheat collection using speed breeding along with gene specific molecular markers [111].
Interestingly, speed breeding can also be integrated with advanced technique like gene editing to precisely alter the plant genes for better coping with various biotic and abiotic stresses in threatening climatic changes. In traditional CRISPR gene editing, the sgRNA directs Cas9 enzymes to cut target sequence. ‘CRISPR-ready’ genotypes containing heterologous Cas9 gene can be created. For instance, a transformant harboring a Cas9 transgene can be used a donor to create a stock of elite inbred lines using speed marker-assisted backcrossing. Such an integrated system like ExpressEdit could circumvent the bottlenecks of in vitro manipulation of plant materials also making gene editing fast-tracking [1]. Integration of both the techniques without tissue culture/foreign DNA requires handful of technological breakthroughs with the desirable outcomes being allelic modification, these would bypass genetically modified organism (GMO) label. It has been widely reported that single or multiplex edits can be obtained [112] and could be implemented with some tissue culture free techniques like CRISPR-Cas9 ribonucleoprotein (RNP) complexes in wheat [91] and maize [90].
Genomic selection (GS) unlike MAS uses genome-wide DNA markers in order to predict the genetic gain of breeding individuals for complex traits such as yield [113]. The effect of large number of genetic variants for such a complex traits is captured through linkage disequilibrium (LD) with the genome-wide markers (SNPs), effects of which are determined in large training populations (lines in which marker genotype and trait are measured). Since speed breeding can substantially lowers down the generation periods, it can maximize the benefits by applying genomic selection at every generation to select parents for next generation. Modern genotyping techniques such as rAmpSeq may considerably reduce the genotyping cost for genomic selection [114]. When combined with speed breeding protocol, the approach for stacking of best haplotypes (ones with desirable resistance alleles/desirable edits) could be used rapidly to develop new cultivars [1] with improved performance across multiple traits like coping with adverse climatic variations or any pathogen/insect attack.
Re-domestication of crop plants for capturing the desirable alleles for climate resilience can be sped up by linking it with speed breeding. Re-creation of the polyploids such as groundnut (Arachis hypogea) and banana (Musa spp.) can be benefitted by such approach. Speed breeding could accelerate re-domestication at multiple selection steps after crossing of diploids followed by colchicine application [115]. Ultimately, it will provide access to novel plant traits for developing cultivars of these crops exhibiting disease resistance and stress adaptation. Also, Gene editing and targeted mutagenesis coupled with speed breeding could prove to be more efficient to create healthier foods by biofortification. For instance, the increased content of vitamin B9 in rice and antinutritional glucosinolates from Brassica seeds etc. [1].
Combining all these tools with speed breeding approach would provide rapid access to desirable alleles and novel variation present in CWR and would accelerate the breeding pipelines to develop more climate resilient varieties (Table 1).
Crop species | Target trait/Improved trait | Technology/ Technique used | Reference |
Rice | Submergence tolerance | MAB | [116] |
Rice | Grain number, dense erect panicles and larger grain size | CRISPR/Cas9 | [117] |
Rice | Maintenance of heterosis | CRISPR/Cas9 | [118, 119] |
Wheat | Heat tolerance | GWAS | [120] |
Wheat | Leaf rust, fusarium head blight and stripe rust resistance | Speed breeding | [121, 122, 123, 124] |
Wheat | Powdery mildew-resistant | CRISPR/Cas9 | [80] |
Finger millet | Salt tolerance | RNA sequencing | [125] |
Sorghum | Low and high nitrogen conditions | RNA sequencing | [126] |
Sugarcane | Drought and chilling resistance | CRISPR/Cas9 | [127] |
Maize | Kernel row number | RNA sequencing | [128] |
Maize | High amylopectin content | CRISPR/Cas9 | [129] |
Cotton | Salt and drought tolerance | GWAS | [130] |
Soybean | Salt and drought tolerance | CRISPR/Cas9 | [131, 132] |
Soybean | Salt tolerance | RNA sequencing | [133] |
Chickpea | Drought, salinity, cold and heavy metal stress resistance | RNA sequencing | [134] |
Lentil | Seedling drought stress resistance | RNA sequencing | [135] |
Tomato | High temperature stress responsiveness | GWAS | [136] |
Tomato | Powdery mildew-resistant | CRISPR/Cas9 | [137] |
Tomato | Longer internodes and lighter green leaves with smoother margins | TALEN | [138] |
Tomato | Short (hairy) roots with stunted meristematic, altered branching and increased yield | CRISPR/Cas9 | [139, 140] |
Tomato | Fruits never turn red, altered firmness | CRISPR/Cas9 | [141] |
Broccoli | Dwarf phenotype | CRISPR/Cas9 | [142] |
Watermelon | Albino phenotype | CRISPR/Cas9 | [143] |
Potato | Reduced steroidal glycoalkaloids in leaves and Undetectable level of reducing sugar in tubers | TALEN | [144, 145] |
Mushroom | Reduced browning | CRISPR/Cas9 | [146] |
Banana | Cold and salt resistance | CRISPR/Cas9 | [147] |
Coconut | Root wilt disease | CRISPR/Cas9 | [148] |
Papaya | Drought, heat and cold resistance | CRISPR/Cas9 | [149] |
Apple | Albino phenotype and Blight resistance | CRISPR/Cas9 | [150, 151] |
Utilization of smart breeding tools and techniques for crop improvement.
In the face of ongoing and projected climate change, including higher temperatures and more erratic climate events across extensive regions over the globe, breeding of crop plants with enhanced yield potential and improved resilience to such environments is crucial for global food security. Improved plant varieties that can withstand diseases and pests with efficient use of fewer resources, exhibiting stable yields amidst stressful climate in near future could only help to achieve the goal of climate resilient agriculture. In order to be able to make contribution in climatic resilience, research attention is indispensable for currently underutilized crop species. The concept of smart breeding largely depends upon generating large breeding populations, efficient high throughput phenotyping, big data management tools and downstream molecular techniques to tackle the vulnerability of crop plants to changing climate (Figure 3). The efficient preservation and conservation of plant genetic resources is also a pre requisite for climate smart breeding. Strategies for capturing the novel variation may include the state of the art tools such as gene editing to directly introduce novel alleles found in wild plants into domesticated crop varieties. Generating new crop cultivars with the capability to tolerate multiple stresses can be achieved with increasing information on their basal physiological and genetic mechanisms. The technological improvements in phenotypic and genotypic analysis, as well as the biotechnological and digital revolution could definitely pave the way for developing and deployment of climate smart varieties in coming times.
Compilation of state-of-the-art genomic, phenomic and computational tools comprising smart breeding approach for climatic resilience in agriculture.
The authors declare they have no conflict of interest.
IntechOpen implements a robust policy to minimize and deal with instances of fraud or misconduct. As part of our general commitment to transparency and openness, and in order to maintain high scientific standards, we have a well-defined editorial policy regarding Retractions and Corrections.
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\\n\\n1. RETRACTIONS
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\\n\\nPublishing of a Retraction Notice will adhere to the following guidelines:
\\n\\n1.2. REMOVALS AND CANCELLATIONS
\\n\\n2. STATEMENTS OF CONCERN
\\n\\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\\n\\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\\n\\n3. CORRECTIONS
\\n\\nA Correction will be issued by the Academic Editor when:
\\n\\n3.1. ERRATUM
\\n\\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\\n\\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n3.2. CORRIGENDUM
\\n\\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\\n\\n4. FINAL REMARKS
\\n\\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\\n\\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\\n\\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\\n\\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\\n\\nPolicy last updated: 2017-09-11
\\n"}]'},components:[{type:"htmlEditorComponent",content:'IntechOpen’s Retraction and Correction Policy has been developed in accordance with the Committee on Publication Ethics (COPE) publication guidelines relating to scientific misconduct and research ethics:
\n\n1. RETRACTIONS
\n\nA Retraction of a Chapter will be issued by the Academic Editor, either following an Author’s request to do so or when there is a 3rd party report of scientific misconduct. Upon receipt of a report by a 3rd party, the Academic Editor will investigate any allegations of scientific misconduct, working in cooperation with the Author(s) and their institution(s).
\n\nA formal Retraction will be issued when there is clear and conclusive evidence of any of the following:
\n\nPublishing of a Retraction Notice will adhere to the following guidelines:
\n\n1.2. REMOVALS AND CANCELLATIONS
\n\n2. STATEMENTS OF CONCERN
\n\nA Statement of Concern detailing alleged misconduct will be issued by the Academic Editor or publisher following a 3rd party report of scientific misconduct when:
\n\nIntechOpen believes that the number of occasions on which a Statement of Concern is issued will be very few in number. In all cases when such a decision has been taken by the Academic Editor the decision will be reviewed by another editor to whom the author can make representations.
\n\n3. CORRECTIONS
\n\nA Correction will be issued by the Academic Editor when:
\n\n3.1. ERRATUM
\n\nAn Erratum will be issued by the Academic Editor when it is determined that a mistake in a Chapter originates from the production process handled by the publisher.
\n\nA published Erratum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n3.2. CORRIGENDUM
\n\nA Corrigendum will be issued by the Academic Editor when it is determined that a mistake in a Chapter is a result of an Author’s miscalculation or oversight. A published Corrigendum will adhere to the Retraction Notice publishing guidelines outlined above.
\n\n4. FINAL REMARKS
\n\nIntechOpen wishes to emphasize that the final decision on whether a Retraction, Statement of Concern, or a Correction will be issued rests with the Academic Editor. The publisher is obliged to act upon any reports of scientific misconduct in its publications and to make a reasonable effort to facilitate any subsequent investigation of such claims.
\n\nIn the case of Retraction or removal of the Work, the publisher will be under no obligation to refund the APC.
\n\nThe general principles set out above apply to Retractions and Corrections issued in all IntechOpen publications.
\n\nAny suggestions or comments on this Policy are welcome and may be sent to permissions@intechopen.com.
\n\nPolicy last updated: 2017-09-11
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