Water quality evaluation results for Ge Lake in 2010.
\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"}]},book:{item:{type:"book",id:"2079",leadTitle:null,fullTitle:"Problems, Perspectives and Challenges of Agricultural Water Management",title:"Problems, Perspectives and Challenges of Agricultural Water Management",subtitle:null,reviewType:"peer-reviewed",abstract:"Food security emerged as an issue in the first decade of the 21st Century, questioning the sustainability of the human race, which is inevitably related directly to the agricultural water management that has multifaceted dimensions and requires interdisciplinary expertise in order to be dealt with. 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The overall status of the global water environment has entered into a new stage exhibiting serious ecological degradation and complex environmental pollution. China is confronting critical water environmental problems, such as increasing pollutant loads and aggravated surface and ground water contamination. Based on the mechanisms of water environment evolution and contaminant transport, as well as theories of the watershed non-point source pollution model, the hydrodynamic model and water-quality model, this study built a comprehensive social-economic-hydrology-water environment model system for investigating water environments in watersheds. This model system can simulate the water cycle of watersheds and water environment quality changing processes, and analyse the utilization of water resources, pollutant discharges and the relationship between quantitative couplings and response connections, as well as reveal the formation mechanism of watershed pollutants and the laws of water environment revolution. This model system has successfully supported the comprehensive watersheds managements in Tai Lake, the middle and lower Han River and East Lake in China.
The load of pollutants entering the water environment has rapidly increased in China since the implementation of open and reform policies in China, while minor progress has been achieved in the mitigation of pollution. Among China\'s seven major river systems, the Songhua River and Huai River are slightly polluted, the Yellow River and the Liao River are moderately polluted and the Hai River is highly polluted. Eutrophication of lakes and reservoirs has become serious and is high in nitrogen and phosphorus. Drinking water may have been polluted with conventional contaminants and new types of toxic pollution, which threatens to health of urban and rural people.
The watersheds water environment model can be used in the simulation and evaluation of water environments, the forecasting and prediction of water quality, and can supplement the establishment of the standardization of pollutant discharges and water quality management. It is an important tool for the water planning, management and scientific research of watersheds. An established watersheds water environment model can describe the pollutant migration and transformation rules over time and in terms of space scale. We can make reasonable predictions about the development of the water environment based on the study of variables and fixed scientific parameters. Therefore, the study of watersheds water environment system simulation technology and especially the integration of other factors such as physical environment and ecology into the model system is one of the core technologies in watersheds water planning and environmental management.
Lake Tai is located in the Yangtze River Delta and is one of the five largest fresh water lakes in China. In China\'s lake district, many sub-lakes form extensive water networks. The Tai Lake basin is China\'s fastest changing and developing region. In recent years, due to the continuous degradation of water quality, eutrophication in Lake Tai has worsened and has had negative impacts on regional socio-economic development.
The total length of the Han River is 1532 kilometres, making it the largest tributary of the Yangtze River. Danjiangkou Reservoir, located in the middle and upper reaches of the Han River, acts as the water source of the South-To-North Water Transfer Project, which channels water from the Danjiangkou Reservoir through Henan and Hebei Province to the serious water shortage district in Beijing-Tianjin, China. It provides water to more than 100 million people along its main canal. The water transfer project reduced the reservoir’s water level, substituting water resources supply and the demand balance of the middle and upper reaches of Han watersheds.
East Lake, located in Wuhan, is the second largest downtown lake in China; however, the lake has become atrophied. Due to a rapidly increasing urban population and intensification of the industrial and agricultural sectors, the lake has received excessive amounts of nitrogen and phosphorus, sourced from surrounding point- and non-point sources. The local authorities have made good progress on point-source pollution control. In order to achieve the overall ecological health of East Lake, the next step is to strengthen the control of non-point source pollution and establish a water management network for East Lake.
In summary, this study presents an adaptable watershed aquatic environment model that can be applied to Lake Tai, middle and lower of Han River, and East Lake. The study will establish: (1) an complex model of a river-lake network to achieve dynamic modelling of the Lake Tai water environment; (2) establish a comprehensive water quality model for a systematic water control project to quantitatively estimate the impact of water transfer; (3) a project for controlling the water quality of middle and downstream Han River; (4) build a non-point, dynamically-sourced 3D mathematically-coupled water quality model according to the characteristics of the received water and boundaries across watersheds, as well as the geography of East Lake. This approach will assist in answering how water contamination developed in each watershed, as well as the evolution patterns of the aquatic environment. The findings will help to improve control of the pollution of watersheds in order to maintain the sustainability of their environmental, social and economic development.
The source of non-point pollution primarily derives from the application of fertilizers, pesticides, effluent irrigation and runoff from urban surfaces. Due to the complexity of these sources, the indetermination of the mechanism, it can be challenging to quantify the formation and load of non-point source pollution. In this instance, establishing models that stimulate watershed environments from time and space perspectives can be the most effective and direct measures. Usery et al. [1] coupled GIS and the distributed non-point source model to stimulate and evaluate watershed contaminants. Bryan [2] suggested a multi-standard evaluation measure in the study of non-point agricultural source pollution in Western Australia. Chowdary et al. [3] applied remote sensing and GIS to stimulate non-point agricultural pollutants and sediments on 2700 ha of land in Jharkhand State. Gikas et al. [4] utilized a SWAT model suitable for non-point source pollution in the Mediterranean region. Wang et al. [5] adapted LEAM, which was designated for modelling land use changes, to non-point source pollution and water quality modelling for the determination of the long-term effect of urbanization on water quality.
Recently, the intensification of non-point source pollution has drawn the increasing attention of the science community. Zhang [6-7] built a model of distributed urban-precipitation-runoff source pollution according to the properties of urban-sourced pollution and its transformation processes for the Project of Urban Water Environment Remediation Wuhan, which analysed pollution patterns resulting from different precipitations and land uses. Qiao et al. [8] coupled river network development DEM grid vector data to supply an alternative means for analysing basic terrain data in a non-point source model. Liu et al. [9] combined RS and GIS technology to construct a watershed non-point source pollution model to calculate pollution loads of various types of pollution sources. Tang et al. [10] applied a SWAT watershed non-point source pollution model to evaluate the effect of measures on water quality improvement in Wenyu River.
Contaminants produced from lands are discharged as a single point source into rivers and lakes. The quantification of the migration and transformation of pollutants requires a dynamic water quality model. In light of the different properties of watersheds, such models study river network water dynamics and quality, including that of lakes and reservoirs.
Many dynamic river network water and quality models have already undergone real-world applications. [11] Mikell [12] scattered the water level of watercourses and the flow of water into calculation points. Arega [13] established a simple two-dimensional waterflow-salinity model and applied obvious TVD limited volumetric calculus to solve the calculation of waterflow-salinity. Peng et al. [14] constructed the model for establishing the Han River\'s water quality ecology numerically, which took into account the effect of the top support action of the water level of the Yangtze River. They then numerically stimulated the migration and transformation of water dynamics, total phosphorus, dissolved oxygen and phytoplankton of the Han River.
The common lake and reservoir water dynamics and quality models are grouped into zero-, one-, two- and three-dimensional modelling and according to specific resolutions. Vejzak [15] tested the effect of eutrophication on phytoplankton dynamic, built an eutrophication model and predicted the effect of different nutritional changes. Asaeda [16] stimulated the effect of large aquatic vegetation degradation on nutrient budget in shallow lakes. Kurup [17] applied the finite difference scheme, where TISAT and CE-QUAL-W2 were coupled and tested in a swan lake estuary. Angelini [18] utilized the ELLOBO model to illustrate three status varieties on reservoirs. AyseMuhammetoglu [19] suggested a three-dimensional model on the effect of advantageous large aquatic vegetation on the quality of water of shallow lakes. Wang et al. [20] applied sourced convection diffusion equation and the water ecology dynamic model, based on the two-dimensional watershed water flow-quality model, and as a result built the shallow water ecological restoration model, which they applied to the study of reservoir restoration in Shenzhen.
The sub-basin was considered as a nonlinear reservoir. We combined the Manning Continuity equation and established the nonlinear hysteresis of surface runoff storage. The Manning Continuity equation can be presented as:
where
where Qp1´is the surface of the slope flow (m3/s) and Qg1´is the flow into the ditch (m3/s).
The flow equation for the sub-valley can be presented as:
where W is the width of the sub-basin type (m), S is sub-basin slope, N is the Manning roughness surface and HP is the hollow lag of storage water depth (mm).
The Von Hoyningen-Hune formula was applied for formulating canopy interception of rainfall:
where
Fill the depression, in the form of incremental is:
where
For infiltration, the Green-Ampt equation can be applied when initial rainfall is stronger than the infiltration amount, as well as when the initial rainfall is less than the infiltration amount. The surface infiltration capacity can be defined as:
when F < Fs:
when
where f is infiltration rate (mm/s), Fp is stable infiltration rate (mm/s), M is the initial saturation (mm/mm), I is the rainfall intensity (mm/s), F is the cumulative infiltration amount (mm), Ks is soil saturated hydraulic conductivity (mm/s), S is the wetting front of capillary suction (mm) and Fs is saturated with the cumulative infiltration amount (mm).
Pollutant accumulation
The cumulative equation can be represented as:
where i is the first type of surface coverage, Ki is dust sedimentation rate (g/m2 day), Lsi is the surface dust (g/m2), K2i is the consumption rate of dust fall (d-1) and T is time (d).
The accumulated amount of dust, where
where
Pollutant washing
The pollutant washing index equation can be represented as:
where
We applied the Saint Venant equations to describe the process of river water dynamics; the fundamental equations can be presented as follows:
Continuity equation is,
The momentum equation is,
where
The pollutants diffusion equation can be presented as:
where
The river network node equation can be presented as:
where
We described the natural water body movement control equation using the continuity equation and momentum equation. Under the rectangular coordinate system, this can be expressed as follows:
Continuous equation is:
X direction momentum equation is:
Y direction momentum equation is:
where u and v are the vertical average velocities on x and y direction, Z is the surface elevation, H is the depth of the water, F is Coriolis force coefficient
The pollutants diffusion equation can be presented as:
where
On the interface section of the one-dimensional and two-dimensional models, we used the one-dimensional model to simulate the river water level and change of flow rate, and took the data as an implicit variable of the two-dimensional lake model. At the same time, water level, flow and concentration showed equivalent conditions for both models, so that the coupling of one-dimensional and two-dimensional models was achieved. Transitional elements were the connecting units between the one-dimensional model and two-dimensional models. The transitional unit grid layout is shown in Figure 1.
One-dimensional and two-dimensional models of connection.
Water connection condition:
Traffic connection conditions:
Water connection conditions:
where
Adapting the hydrodynamic and water quality model equation to a unified form can be presented as:
We used non-orthogonal and non-staggered grids to adopt the processing of the wind convection format within the control body, to integral and discrete on the formula(24), and to obtain the discrete equation of convection diffusion as follows:
where
We used the SIMPLE orthogonal algorithm to establish the
The velocity correction equation can be presented as:
where
The surface and velocity equations both belong to diagonal algebraic equations, where the SIMPLE algorithm can be applied for finding a rapid solution.
Lake Tai basin is located in the delta plain area of Yangtze River and its watershed area is 36 500 km2 [21]. Lake Tai basin is China\'s fastest changing and development region and its serious water pollution is a top concern. In investigating the complexity of the Lake Tai watershed, this study only selected Ge Lake, which is in the west of Lake Tai.
The study area is located in upstream Lake Tai and is a major pollutant source of Lake Tai (as shown in Figure 2).The local climate is subtropical and experiences moist marine monsoons. It has abundant rainfall and sunlight hours annually. [22]
Location map of the Ge Lake river network of the Lake Tai Basin.
The local economy of the studied area is quite well-developed, with steady agronomy and rapidly increasing industrial economy. The local GDP per capita was 15,900 CNY in 2000 and 63 000 CNY in 2008, which was three times higher than the national average. [23]
The results of water quality evaluation are shown in Table 1, where TP, TN and COD exceed largely when compared with the normal standard. The properties of organic pollution are highlighted.
\n\t\t\t\tPeriod\n\t\t\t | \n\t\t\t\n\t\t\t\tNH3-N\n\t\t\t | \n\t\t\t\n\t\t\t\tCOD\n\t\t\t | \n\t\t\t\n\t\t\t\tTN\n\t\t\t | \n\t\t\t\n\t\t\t\tTP\n\t\t\t | \n\t\t\t\n\t\t\t\tWater quality\n\t\t\t\t \n\t\t\t\tcategory\n\t\t\t | \n\t\t
Dry season | \n\t\t\tGrade II | \n\t\t\tGrade V | \n\t\t\tWorse than Grade V | \n\t\t\tGrade III | \n\t\t\tWorse than Grade V | \n\t\t
Normal season | \n\t\t\tGrade IV | \n\t\t\tGrade IV | \n\t\t\tWorse than Grade V | \n\t\t\tGrade IV | \n\t\t\tWorse than Grade V | \n\t\t
Wet season | \n\t\t\tGrade II | \n\t\t\tGrade V | \n\t\t\tWorse than Grade V | \n\t\t\tGrade IV | \n\t\t\tWorse than Grade V | \n\t\t
Water quality evaluation results for Ge Lake in 2010.
The water quality is worse than Grade V at Ge Lake, with atrophied nutrition and the major pollution indicators of COD and NH3-N, as well as TN and TP. The lake water quality has declined from Grade III in the early 1990s to the poorer Grade V class and the lake\'s pollution index has risen 10% per year on average. Presently, algal species have become dominant and the lake has changed from being a grass-type lake into an algae-type lake.
Combined with the features of the study area, in view of the mixture of pollutants present, a one-dimensional and two-dimensional mathematical model, respectively, were chosen to describe the hydrodynamic processes of the river and the transformation processes of pollutant migration. The finite control volume method was applied to solve the control equation of the two-dimensional hydrodynamic- and water-quality model (see Section 2). To generalize the research area, typical Ge-Lake watershed hydrology and water quality data were applied, which assisted in calibrating and verifying the model.
The river network in the study area was divided into different calculation sections, including 110 river sections, 91 calculation nodes and 519 calculation sections. The river network generalization results are shown in Figure 3.
The typical river network profile in Ge-Lake.
The distribution map of verification monitoring points in Ge Lake is shown in Figure 4.
Distribution map of verification monitoring points in Ge Lake.
Using daily water flow data from Xiaxi bridge station (Xiaxi River), Huangli station (Huangli River) and Dong’an bridge station (Beigang River) in 2007, we were able to calculate water outflow and input at every river sections. This was then applied to the verification of hydrodynamics at the upper-boundary conditions. Using daily water level data from Dukou station, Dapukou station and Yixing station at Lake Tai in 2007, we were able to calculate water output level at every river sections.This was then applied for the verification of hydrodynamics at lower-boundary conditions. We used pollution discharge load data in 2007 for the verification of the water quality model.
Correlation parameters
Roughness coefficient
The empirical formula and the result of the model parameter calibration determined the roughness coefficient n. River roughness was 0.02 ~ 0.025 and Ge-Lake had roughness of approximately 0.022.
Diffusion coefficient
The diffusion coefficient of the body of water in the study area (Ex) was obtained using an empirical formula according to Lagrange turbulence length and the intensity of turbulence length concept. The Ex values can be expressed as:
where
Degradation coefficient
The degradation coefficient (K0) of NH3-N, COD, TN and TP are shown in Table 2.
\n\t\t\t\tParameter\n\t\t\t | \n\t\t\t\n\t\t\t\tNH3-N\n\t\t\t | \n\t\t\t\n\t\t\t\tCOD\n\t\t\t | \n\t\t\t\n\t\t\t\tTN\n\t\t\t | \n\t\t\t\n\t\t\t\tTP\n\t\t\t | \n\t\t
\n\t\t\t\tK\n\t\t\t\t0 (d-1) | \n\t\t\t0.03-0.15 | \n\t\t\t0.02-0.13 | \n\t\t\t0.05-0.08 | \n\t\t\t0.05 | \n\t\t
Degradation coefficient (K0) of NH3-N, COD, TN and TP.
Model validation
Hydrodynamic model validation
Daily water flow and water level data (2007) from Huangnianqiao station (Taige Canal) and Caoqiao station (Caoqiao River) was used to verify the water dynamics of the model.
flow verification
The results of flow verification are shown in Figures 5 and 6. Overall, changes in flow calculation were consistent with changes in the measured data.
Flow verification of Huangnianqiao section.
Flow verification of Caoqiao section.
Water level validation
The results of water level verification are shown in Figures 7 and 8. The precision of the model was good and it was able to meet the needs of the calculation.
Water level verification of Huangnianhe section.
Water level verification of Caoqiao section.
The two-dimensional hydrodynamic model used daily water level data from Fangqian station (Ge Lake) for verification. It showed that the rule of calculated values and measured values had good consistency. Validation results are shown in Figure 9.
Water level verification of Ge-Lake.
Water quality model validation
Choosing three section stations within the scope of the study area, i.e., Huangnianqiao, Xuenianqiao and Cao Qiao, we used monthly-measured water quality data in 2007 to validate the river network model. The calculated results were compared with the measured values. Model simulated values and measured values were consistent, matching the requirements for the river\'s one-dimensional water quality simulation.
\n\t\t\t\tSections\n\t\t\t | \n\t\t\t\n\t\t\t\tRelative Error\n\t\t\t | \n\t\t|||
\n\t\t\t\tNH3-N\n\t\t\t | \n\t\t\t\n\t\t\t\tCOD\n\t\t\t | \n\t\t\t\n\t\t\t\tTN\n\t\t\t | \n\t\t\t\n\t\t\t\tTP\n\t\t\t | \n\t\t|
Huangnianqiao Section | \n\t\t\t23.98 | \n\t\t\t18.92 | \n\t\t\t23.49 | \n\t\t\t21.56 | \n\t\t
Xuenianqiao Section | \n\t\t\t34.56 | \n\t\t\t31.6 | \n\t\t\t26.28 | \n\t\t\t30.54 | \n\t\t
Caoqiao Section | \n\t\t\t20.5 | \n\t\t\t16.68 | \n\t\t\t22.65 | \n\t\t\t23.56 | \n\t\t
Average error of all section indexes (%).
We selected north Ge Lake as a regular water quality monitoring site. We validated the two-dimensional lake water quality model results by using measured data from January, April, July and October, 2007. As shown in Table 4, by comparing the simulated values and measured values, NH3-N, COD, TN and TP in Ge Lake indicated that the relative errors were mostly less than 30% and the average error was roughly 24%.
\n\t\t\t\tTime\n\t\t\t | \n\t\t\t\n\t\t\t\tRelative Error\n\t\t\t | \n\t\t|||
\n\t\t\t\tNH3-N\n\t\t\t | \n\t\t\t\n\t\t\t\tCOD\n\t\t\t | \n\t\t\t\n\t\t\t\tTN\n\t\t\t | \n\t\t\t\n\t\t\t\tTP\n\t\t\t | \n\t\t|
2007-1 | \n\t\t\t18.92 | \n\t\t\t20.11 | \n\t\t\t14.66 | \n\t\t\t17.39 | \n\t\t
2007-4 | \n\t\t\t28.23 | \n\t\t\t20.45 | \n\t\t\t26.12 | \n\t\t\t11.54 | \n\t\t
2007-7 | \n\t\t\t29.03 | \n\t\t\t21.55 | \n\t\t\t13.24 | \n\t\t\t24.48 | \n\t\t
2007-10 | \n\t\t\t36.11 | \n\t\t\t37.08 | \n\t\t\t22 | \n\t\t\t34.51 | \n\t\t
Relative error of all indexes (%).
This study established a complex lake-river network coupled with a one- and two-dimensional water quality model under human interference. The research was based on socio-economic, demographical, climatic and hydrological data from 2007 at Ge Lake (Lake Tai region). Chemical oxygen demand, ammonium-N, total nitrogen and total phosphorus were selected as water quality indicators. The study analysed the water environment evolution rules as affected by human activities and interference. The results showed that ammonium-N and COD were mainly derived from domestic waste and industrial point sources; TN was primarily sourced from domestic waste and agricultural non-point sources; TP was largely derived from domestic and aquaculture pollution.
In view of the lakes in the region that were included in the river network, this study established a water environment holding capacity calculation model; the research findings are fairly adaptive to wider application in terms of water resource conservation and environmental management. The study also introduced a new angle for dynamic water-holding capacity modelling research. This approach can be integrated into pollutant control and environmental monitoring technologies in order to achieve the overall health of the water environment.
At a length of 1531 km, the Han River is the largest tributary of the Yangtze River. The middle- and lower-Han rivers begin at the downstream end of the Danjiangkou Reservoir and flows for about 650 km before joining the Yangtze River at Wuhan City (see Figure 10). The total area of the drainage basin is about 63 800 km2. The river\'s reaches have been extensively dammed since the 1950s and its course has been highly regulated by a series of coupled reservoirs, among which Danjiangkou Reservoir is the largest; also contributing to this is the water source for the Middle Route Project (MRP) for the South-to-North Water Diversion (SNWD) Project, about 9.5 billion m3 of water will be extracted from the Danjiangkou Reservoir and supplied to northern China annually, amounting to about 30% of the current annual downstream discharge. In order to mitigate the project’s effects, the Yangtze-Hanjiang Water Diversion Project (YHWD) is supposed to transfer water from the upstream of the Yangtze River to the downstream of the Hanjiang River near Qianjiang City with a maximum capacity of 500 m3/s.
The cascade reservoirs and the inter-basin water diversion projects, i.e. the MRP and the YHWD, lead to profound and complex effects in the middle and lower Hanjiang River
Location map for the middle and lower Han River Basin.
A coupled one-dimensional hydrodynamic- and water-quality model was developed in a bid to assess water quality and quantity in the middle- and lower-Han rivers as a result of the impact of the operation of the cascade reservoirs and inter-basin water diversion projects. The systematic model includes numerical descriptions of the hydrodynamic and pollutant transport processes, the loads of different water users, stream-aquifer interactions, as well as the operations of cascade reservoirs and inter-basin water transfer projects (see Chapter 2).
The middle and lower Han rivers were divided into five reaches with 157 sections and the area outside the river was divided into 18 sub-hydrological units. A schematic illustration of the watershed is shown in Figure 11.
Schematic illustration of the basin.
Parameters
Manning’s roughness coefficient n1d took on the values of 0.03 to 0.055 according to a hydrological report from the Department of Water Resources of Hubei Province.
The value of the longitudinal dispersion coefficient ranged from 0.05 to 1 m2/s, according to the national standard of the Technical Guide and Standard of Environmental Impact Assessment issued by China\'s Ministry of Environmental Protection. The degradation coefficients of pollutants are shown in Table 5.
\n\t\t\t\tPollutant\n\t\t\t | \n\t\t\t\n\t\t\t\tCODMn\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\tNH3-N\n\t\t\t | \n\t\t\t\n\t\t\t\tTP\n\t\t\t | \n\t\t
Degradation coefficient | \n\t\t\t0.25-0.3 | \n\t\t\t0.33-0.35 | \n\t\t\t0.06-0.09 | \n\t\t
Degradation coefficients of different pollutants (d-1).
Calibration and validation
The monitoring data of hydrodynamics and water quality from 2005 were used for calibrating the model. CODMn, NH3-N and TP were chosen as water-quality indexes. The hydrodynamics and water quality were simulated along five monitoring sections using data from 2006 to 2007 to validate the model. The average relative deviations of the water levels between the simulated values and the measured values were within 1% of each other. The average relative deviations of the computed CODMn, NH3-N and TP concentrations vs. the measured values were less than 20%. The model could therefore accurately simulate hydrodynamic conditions and water quality.
Hydrodynamic validation results of typical control cross-section 3.
Water-quality validation results of typical control cross-section 3 (calculated and measured concentration of CODMn, NH3-N and TP).
The long-term series of hydrological data from 1956 to 1998 were analysed to evaluate the long-standing trends in water quantity and quality of the middle and lower Han rivers. Two hydrological conditions were involved: the existing hydrological conditions prior to starting inter-basin water division projects and future hydrological-hydrodynamic conditions considering the cascade reservoirs and inter-basin water diversion projects (after initiating the projects).
Based on the daily water demand/supply balance calculation, the discharge volume along the stream of the middle and lower Han rivers was analysed. Six sections in the middle and lower reaches were selected as control sections for six major cities along the river. Sections 1, 3, 5, 6, 8 and 13 respectively, represented the conditions of Danjiangkou, Xiangfan, Jingmen, Shayang, Qianjiang and Wuhan reaches.
Table 6 shows the average annual discharges of the six control sections prior to and following the projects. According to the calculated results, the problem of water deficiency will be serious for the middle reaches of the Han river in the future, as the operation of the MRP will significantly decrease stream discharge. As shown in Tables 7 and 8, operation of the cascade reservoirs and the MRP will significantly impact the hydrological/hydrodynamic conditions of the middle and lower Han rivers. The average water level will be decreased by 0.23 to 0.39 m in the river reaches downstream of Danjiangkou Reservoir, except for section 3 and section 6, which are controlled by the operation of Cuijiaying Reservoir and Xinglong Reservoir.
\n\t\t\t\tControl section\n\t\t\t | \n\t\t\t\n\t\t\t\tBefore the projects (m3/s)\n\t\t\t | \n\t\t\t\n\t\t\t\tAfter the projects (m3/s)\n\t\t\t | \n\t\t\t\n\t\t\t\tReduction\n\t\t\t | \n\t\t
1 | \n\t\t\t1161.6 | \n\t\t\t811.3 | \n\t\t\t-350.3 | \n\t\t
3 | \n\t\t\t1292.7 | \n\t\t\t912.7 | \n\t\t\t-380.0 | \n\t\t
5 | \n\t\t\t1518.0 | \n\t\t\t1157.2 | \n\t\t\t-360.8 | \n\t\t
6 | \n\t\t\t1497.8 | \n\t\t\t1139.3 | \n\t\t\t-358.5 | \n\t\t
8 | \n\t\t\t1341.5 | \n\t\t\t1195.2 | \n\t\t\t-146.3 | \n\t\t
13 | \n\t\t\t1277.0 | \n\t\t\t1140.0 | \n\t\t\t-137.0 | \n\t\t
Comparison of the average annual discharge of the control sections before/after the SNWD and YHWD projects.
Calculated daily discharge of section 1 before/after the projects.
Calculated daily discharge of section 3 before/after the projects.
Calculated daily discharge of section 13 before/after the projects.
\n\t\t\t\tControl section\n\t\t\t | \n\t\t\t\n\t\t\t\tBefore the projects (m)\n\t\t\t | \n\t\t\t\n\t\t\t\tAfter the projects (m)\n\t\t\t | \n\t\t\t\n\t\t\t\tReduction\n\t\t\t | \n\t\t
1 | \n\t\t\t87.23 | \n\t\t\t86.85 | \n\t\t\t-0.38 | \n\t\t
3 | \n\t\t\t58.67 | \n\t\t\t62.78 | \n\t\t\t4.11 | \n\t\t
5 | \n\t\t\t40.04 | \n\t\t\t39.65 | \n\t\t\t-0.39 | \n\t\t
6 | \n\t\t\t33.47 | \n\t\t\t36.35 | \n\t\t\t2.88 | \n\t\t
8 | \n\t\t\t29.5 | \n\t\t\t29.27 | \n\t\t\t-0.23 | \n\t\t
11 | \n\t\t\t24.29 | \n\t\t\t24.03 | \n\t\t\t-0.26 | \n\t\t
Comparison of the average annual water level of the control sections before/after the SNWD and YHWD projects.
\n\t\t\t\tControl section\n\t\t\t | \n\t\t\t\n\t\t\t\tBefore the projects (m/s)\n\t\t\t | \n\t\t\t\n\t\t\t\tAfter the projects (m/s)\n\t\t\t | \n\t\t\t\n\t\t\t\tReduction\n\t\t\t | \n\t\t
1 | \n\t\t\t0.73 | \n\t\t\t0.59 | \n\t\t\t-0.14 | \n\t\t
3 | \n\t\t\t0.9 | \n\t\t\t0.31 | \n\t\t\t-0.59 | \n\t\t
5 | \n\t\t\t1.11 | \n\t\t\t0.97 | \n\t\t\t-0.14 | \n\t\t
6 | \n\t\t\t0.69 | \n\t\t\t0.32 | \n\t\t\t-0.37 | \n\t\t
8 | \n\t\t\t0.74 | \n\t\t\t0.72 | \n\t\t\t-0.02 | \n\t\t
11 | \n\t\t\t0.67 | \n\t\t\t0.64 | \n\t\t\t-0.03 | \n\t\t
Comparison of the average annual flow velocity of the control sections before/after the SNWD and YHWD projects.
The results showed that the middle-lower reaches of the Han river will have similar trends of water-quality degradation. The average concentration of CODMn in the river was 2.61 mg/L, NH3-N 0.52 mg/L and TP 0.16 mg/L with existing hydrological-hydrodynamic conditions; with future operation of the cascade reservoirs and inter-basin water diversion projects, the average concentration of CODMn will increase by 55% to 4.04 mg/L, NH3-N by 48% to 0.78 mg/L and TP by 46% to 0.24 mg/L.
Following the completion of the projects, the water quality will be degraded to Class IV. This result indicates that the operation of the YHWD project will be unable to mitigate water-quality deterioration in the lower Han River. The model simulations indicated that the dual effects of the projects, i.e., increasing the nutrient concentration and decreasing the flow velocity, could induce an environment favourable for algal growth. Thus, large-scale blooms likely occurred, even if only in the middle reaches of the river.
\n\t\t\t\tSections\n\t\t\t | \n\t\t\t\n\t\t\t\tCODMn\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\tNH3-N\n\t\t\t | \n\t\t\t\n\t\t\t\tTP\n\t\t\t | \n\t\t|||
\n\t\t\t | \n\t\t\t\tBefore the projects\n\t\t\t | \n\t\t\t\n\t\t\t\tAfter the projects\n\t\t\t | \n\t\t\t\n\t\t\t\tBefore the projects\n\t\t\t | \n\t\t\t\n\t\t\t\tAfter the projects\n\t\t\t | \n\t\t\t\n\t\t\t\tBefore the projects\n\t\t\t | \n\t\t\t\n\t\t\t\tAfter the projects\n\t\t\t | \n\t\t
1 | \n\t\t\t2.08 | \n\t\t\t3.09 | \n\t\t\t0.14 | \n\t\t\t0.22 | \n\t\t\t0.03 | \n\t\t\t0.04 | \n\t\t
2 | \n\t\t\t3.18 | \n\t\t\t4.67 | \n\t\t\t0.75 | \n\t\t\t1.09 | \n\t\t\t0.14 | \n\t\t\t0.20 | \n\t\t
4 | \n\t\t\t3.04 | \n\t\t\t4.47 | \n\t\t\t0.68 | \n\t\t\t1.00 | \n\t\t\t0.15 | \n\t\t\t0.22 | \n\t\t
5 | \n\t\t\t2.97 | \n\t\t\t4.38 | \n\t\t\t0.61 | \n\t\t\t0.91 | \n\t\t\t0.16 | \n\t\t\t0.23 | \n\t\t
6 | \n\t\t\t2.65 | \n\t\t\t3.99 | \n\t\t\t0.54 | \n\t\t\t0.82 | \n\t\t\t0.16 | \n\t\t\t0.23 | \n\t\t
7 | \n\t\t\t2.62 | \n\t\t\t3.95 | \n\t\t\t0.53 | \n\t\t\t0.81 | \n\t\t\t0.16 | \n\t\t\t0.23 | \n\t\t
8 | \n\t\t\t2.38 | \n\t\t\t4.04 | \n\t\t\t0.47 | \n\t\t\t0.73 | \n\t\t\t0.16 | \n\t\t\t0.24 | \n\t\t
9 | \n\t\t\t2.30 | \n\t\t\t3.89 | \n\t\t\t0.45 | \n\t\t\t0.70 | \n\t\t\t0.15 | \n\t\t\t0.24 | \n\t\t
10 | \n\t\t\t2.17 | \n\t\t\t3.65 | \n\t\t\t0.42 | \n\t\t\t0.65 | \n\t\t\t0.17 | \n\t\t\t0.24 | \n\t\t
11 | \n\t\t\t2.47 | \n\t\t\t3.94 | \n\t\t\t0.48 | \n\t\t\t0.71 | \n\t\t\t0.17 | \n\t\t\t0.25 | \n\t\t
12 | \n\t\t\t2.51 | \n\t\t\t3.86 | \n\t\t\t0.48 | \n\t\t\t0.68 | \n\t\t\t0.17 | \n\t\t\t0.25 | \n\t\t
13 | \n\t\t\t2.49 | \n\t\t\t3.83 | \n\t\t\t0.47 | \n\t\t\t0.68 | \n\t\t\t0.17 | \n\t\t\t0.25 | \n\t\t
14 | \n\t\t\t2.66 | \n\t\t\t3.92 | \n\t\t\t0.47 | \n\t\t\t0.67 | \n\t\t\t0.18 | \n\t\t\t0.26 | \n\t\t
15 | \n\t\t\t2.47 | \n\t\t\t3.98 | \n\t\t\t0.47 | \n\t\t\t0.67 | \n\t\t\t0.18 | \n\t\t\t0.26 | \n\t\t
Comparison of the simulated water quality of the main sections before/after the SNWD and YHWD projects.
Calculated daily concentrations of COD, NH3-N and TP of section 4 before/after the projects
A combined water-quantity-quality model was applied to simulate the current and future characteristics of water quantity and water quality in the middle and lower Han rivers. The simulation results showed that the implementation of the MRP and the operation of cascade reservoirs will exacerbate the problems of water pollution and water deficiency in the middle and lower reaches of the river. This model can assist water resource managers to make the right decisions about water improvement measures.
East Lake is constituted by Guozheng Lake, Hou Lake, Miao Lake, Shuiguo Lake, Tangling Lake and others. Its geological structure is complex; the length of East Lake is 11.5 km and its average width 2.9 km. East Lake is connected to the Yangtze River via the Castle Peak port.
East Lake lies in Wuchang, which is the political, cultural and information centre of Hubei Province.
We built the non-point source model based on the digital elevation map (DEM). On the basis of hydraulics and the water quality model, we established the East Lake three-dimensional hydrodynamic and water quality model equation (see Chapter 2).This scenario was based on 2006 calibration results. After removing the point sources, we used the non-point source data to simulate the water quality status of East Lake from 2007 to 2010.
East Lake was divided into five reaches and the quantity of grid measured as 47 x 62; a schematic illustration of the watershed is shown in Figure 18.
The division map for East Lake.
According to the measured data of East Lake from 2000 to 2005, we calibrated parameters, such as roughness coefficient, nitrogen degradation coefficient, organophosphate degradation coefficient in this study.
Using the measured results of water level and water quality from 2006 as initial conditions for control within the model, we calculated the changing process of flow, water level, water temperature, as well as the water quality of TP, TN, NH4, DO.
Through a survey about the pollution circumstances of East Lake, we confirmed 22 outlets surrounding the main point source and 61 non-point source outlets. Their positions are shown in Figure 19.
The source of pollutants map for East Lake.
In this research, we used measured data of East Lake from 2006 to verify the model.
Location map of East Lake.
The model was verified as follows:
Results for Miao Lake verification are shown in Figures 21 to 24 below:
Verification chart for Miao Lake temperature.
Verification chart for Miao Lake DO.
Verification chart for Miao Lake TN.
Verification chart for Miao Lake TP.
Verification chart for Tangling Lake temperature.
The verification chart for Tangling Lake DO.
The verification chart for Tangling Lake TN.
Verification chart for Tangling Lake TP.
The verification charts show that the maximum error of simulated calculation was less than 20% when compared to the measured concentration. The relative error in about 96% of samples was less than 10%. Results showed that the model was able to meet the requirements of simulation research.
The simulation results showed that the management of point sources will improve the water quality of East Lake, but that the effect will not be obvious in the short-term. The management of non-point source pollution should also be strengthened.
We conducted our research by including two study lakes. The results showed that the water quality of Miao Lake improved significantly, while Tang Ling Lake’s did not.
Water quality simulation of Miao Lake.
Water quality simulation of Tangling Lake.
The present research established a three-dimensional layered water hydrodynamic numerical model of East Lake. Using the measured data of hydrology and water quality from 2006, we completed the model parameters for calibration and validation. Then, we analysed the water quality and drew the following conclusion: the water quality of East Lake region must be improved. Factors such as non-point source and sediment need to be considered in the overall consideration.
The watersheds water environment system model established in this study has a solid theoretical basis and was successfully applied in the water environment response and simulation analysis of Tai Lake, the middle and lower Han rivers and East Lake.
We established a two-dimensional interfaced complex model of the river-lake network coupled with water quality, which was applied to the river network of Lake Tai using a series of conditions related to social economy, population, meteorological and hydrological factors measured in 2007. The research findings are fairly adaptive to wider application in terms of water resource conservation and environmental management.
We used the coupled water-quantity-quality model to simulate the current and future characteristics of water quantity and water quality for the middle and lower Han rivers. The model can assist administrators decision-making concerning water improvement measures.
Based on the hydraulic and water quality models, we created a three dimensional hydrodynamic and water quality model that can be successfully applied to the simulation of non-point source pollution, thereby providing a scientific tool for water environment management work.
Blood glucose is regulated by the pancreatic hormones alone or in combination with other endocrine glands and all this is controlled by one or more gene or cellular or molecular targets. If any problem occurs in the normal pathway(s), then multiple drugs or therapies are used to cure it. Moreover with the emerging technologies, multiple plant based formulations has been synthesized or in process to cure all blood glucose regulation problems and their associated diseases.
\nIt is located at the back of stomach, within left upper abdominal cavity.
\nIts parts are head, body and tail. Majority of this secretory organ consists of:
Acinar/exocrine cells: Which secrete pancreatic juice (containing digestive enzymes i.e. amylase, pancreatic lipase and trypsinogen) into main and accessory pancreatic duct.
Endocrine cells: Which secrete pancreatic hormones directly in blood stream (in endocrine way). These cells cluster together and form the so-called islets of Langerhans (small, island-like structures within the exocrine pancreatic tissue and accounts for only 1–2% of the entire organ) (Figure 1). These are five different types of cells and release various hormones [1]:
Glucagon-producing α-cells: They are 15–20% of the total islet cells and releases Glucagon to increase blood glucose levels.
Amylin-, C-peptide- and insulin-producing β-cells: They are 65–80% of the total cells and produces insulin to decrease glucose.
Pancreatic polypeptide (PP)-producing γ-cells: 3–5% of the total islet cells, to regulate the exocrine and endocrine secretion activity of the pancreas, is made of them.
Somatostatin-producing δ-cells: Constitute 3–10% of the total cells and releases Somatostatin which inhibits both, glucagon and insulin release.
Ghrelin-producing ɛ-cells: Comprise <1% of the total islet cells.
Anatomical organization of the pancreas.
Pancreas maintains blood glucose levels within a very narrow range (4–6 MM) through glucagon and insulin by their opposing and balanced actions by the phenomenon of glucose homeostasis. During sleep/between meals/when blood glucose levels are low/during prolonged fasting, α-cells release glucagon and promote hepatic glycogenolysis. Along with this, glucagon do hepatic and renal gluconeogenesis and increase endogenous blood glucose levels. In elevated exogenous glucose levels, after a meal, insulin secretion is stimulated from β-cells and after docking to its receptor on muscle and adipose tissue, insulin enables insulin-dependent uptake of glucose into tissues and lowers blood glucose levels by removing the exogenous glucose from the blood stream (Figure 2). Moreover insulin enhances glycogenesis, lipogenesis and incorporation of amino acids into proteins; thus it performs its anabolic action as compared to glucagon which is catabolic. Along with pancreas, other organs also regulate blood glucose levels (Figure 3).
\nMaintenance of blood glucose levels by glucagon and insulin.
Maintenance of blood glucose levels by different organs (a) during well fed state (b) during post-prandial state.
Genetics is identifying a whole new set of genes, proteins and pathways that are related to diabetes and blood sugar control. Till now, scientist have identified a genetic disorder in MafA (it controls the production of insulin in β-cells). Surprisingly, this genetic defect was present in an unrelated family along with diabetic and insulinoma family members. The link of this gene with a defect was detected for the first time and a stable resultant mutant protein was found with a longer life in the cell, and found to be significantly more abundant in β-cells than its normal version [2].
\nGene on chromosome-2 {encodes glucose-6-phosphatase catalytic 2 (G6PC2)} is linked with fasting glucose levels and is primarily expressed in pancreatic β-cells to convert glucose-6-phosphate back to glucose. Its genetic variation may be responsible for reduction in insulin secretion that increases glucose concentration. Chronically elevated levels of glucose may be a precursor for type 2 diabetes [3].
\n13 new genetic variants has been discovered by an international research consortium and these variants can manipulate blood glucose regulation, insulin resistance and function of insulin-secreting β-cells in European descent populations, in which 05 of the following newly discovered variants raised the risk of developing type 2 diabetes:
SNPs in the region of ADCY5 which influence fasting and postprandial glucose levels.
FADS1 which is linked with fasting glucose as well as lipid traits.
Only one variant, near IGF1 which is associated with insulin resistance
β-cell impairment, which may play a larger role in type 2 diabetes than previously recognized
Environment which may contribute to insulin resistance more than it does to insulin secretion.
By using high-density microarray analysis, more than 31,000 genes, linked with pancreas, have been discovered and main aim was to find which gen(s) were most sensitive to glucose and fatty acids particularly from the products of high fat and sugar diets. It was found that TNFR5 gene had maximum compassion to glucose and fatty acids and due to high levels of fat and sugar, beta cells are destroyed due to its over expression. These findings suggested that people with type-II diabetes, primarily with poor blood glucose management/who have not been diagnosed, are more likely to over express this gene that leads to β cell damage. But blocking of TNFR5 in beta-cells, especially when glucose and fatty acids consumption is high, halted their obliteration which shows that reticence of TNFR5 activity could be a promising treatment strategy against type 2 diabetes [4].
\nTo identify genetic variants responsible for blood sugar control, a genome-wide association study was done to find SNPs which could be correlated with Fasting Plasma Glucose levels. It was found that most strongly associated SNP was rs560887 in initial sampling of 650 non-obese French people. Same SNP was correlated with FPG levels in a secondary sample of 3400 same people, approximately 5000 Finns and a group of 860 obese French children. When results of all studied samples were combined, researchers found that each copy of T version of rs560887 leads to a 0.06 mmol/L reduction in FPG while rs560887 did not correlate with insulin levels or BMI of subjects. Moreover even after a 9 year follow-up period in French samples, this SNP also could not correlate with the risk of type 2 diabetes. Moreover two other SNPs; rs1260326 and rs1799884 (previously found to be associated with FPG) were also found to be significantly associated with FPG levels in same study and it was concluded that genes affected by these SNPs affect the threshold level of glucose in the bloodstream and triggered secretion of insulin by pancreas. When threshold will be higher, level of blood glucose increase even before insulin starts to regulate it [5].
\nThese are class B-GPCRs which are important targets for drugs of type 2 diabetes, obesity and blood glucose regulation problems. Structures of several class A-GPCRs have been solved, but class B receptors have not been well studied because of technical challenges. Their structures were identified and reported by four international research teams; NIDDK, NIGMS, FDA and NIDA. Structure of Glucagon receptor helps to understand how different domains cooperate in modulating the receptor function at molecular level. GLP-1 receptor, identified by cryo-electron microscopy, examined structure of receptor in complex with GLP-1 and its coupled G-protein while detailed structure of GLP-1 receptor, when bound by small molecules (that affect receptor’s activity) has also been given and it is difficult to expect the importance of GPCRs which are targeted by about half of all drugs. Structural information about these receptors is crucial for further drug discovery efforts [6].
\nControl of blood glucose depends heavily on G-protein-coupled receptors (GPCRs) which can span cell membranes to communicate signals from the outside to inside of cell and starts a cascade of reactions in cell when once activated by binding of a substance which had made these receptors an important target for drug development. When blood glucose drops after an overnight fast, pancreas releases glucagon which binds a GPCR, glucagon receptor, on liver and muscle cells and stimulates cells to release glucose in blood. Moreover glucagon-like peptide-1 (GLP-1) hormone works by binding to another GPCR, GLP-1 receptor, on pancreatic cells. After a meal, intestine produces GLP-1, which leads to the production of insulin from pancreas to stimulate cells to pick glucose from blood [7].
\nFor many years, heterocyclic scaffolds were the basis of anti-diabetic chemotherapies as bioactive scaffolds and have been evaluated for their biological response as inhibitors against their respective anti-diabetic molecular targets over past 5 years (2012–2017). Results revealed a diverse target sets of these scaffolds including protein tyrosine phosphatase 1 B (PTP1B), dipeptidyl peptidase-4 (DPP-4), free fatty acid receptors 1 (FFAR1), G protein-coupled receptors (GPCR), peroxisome proliferator activated receptor-γ (PPARγ), sodium glucose co-transporter-2 (SGLT2), α-glucosidase, aldose reductase, glycogen phosphorylase (GP), fructose-1,6-bisphosphatase (FBPase), glucagon receptor (GCGr) and phosphoenolpyruvate carboxykinase (PEPCK) [8].
\nIn addition to other several even newer therapies in development, Incretin- based therapies, like dipeptidyl peptidase- 4 (DPP- 4) inhibitor and glucagon like peptide-1 (GLP-1) analogues/mimetic offer a new therapeutic means for the treatment of T2DM. Moreover a great attention has been focused by many researchers on a number of potential molecular targets in adipocytes e.g. adipokines [8].
\nIn β-cells, main stimulus for insulin release increases blood glucose levels after a meal. This blood glucose is taken up by facilitative glucose transporter GLUT2 (SLC2A2) on the surface of β-cells. Once inside the cell, glucose undergoes glycolysis and an amplified ATP/ADP ratio and this distorted ratio leads to close ATP-sensitive K+-channels (KATP-channels). While in non-stimulated circumstances, these channels open to ensure the maintenance of resting potential by transporting K+-ions down their concentration gradient out of the cell. Upon closure, succeeding decrease in potency of externally moved K+-current elicits depolarization of membrane, followed by opening of voltage-dependent Ca+-channels (VDCCs). Increase in intracellular Ca+ concentrations ultimately triggers fusion of insulin-containing granules with membrane and succeeding release of their content. Whole secretory process is biphasic and 1st phase lasts for around 5 minutes after the glucose stimulus with the release of majority of insulin while in 2nd phase, which is somewhat slower, the remaining insulin is released. This insulin is stored in large dense-core vesicles which are recruited near plasma membrane immediately after stimulation so that it should be readily available. Key molecules that mediate the fusion of the insulin-containing large dense-core vesicles belong to the superfamily of the soluble N-ethylmaleimide-sensitive factor attachment protein (SNAP) receptor proteins (SNAREs).which are:
Synaptosomal-associated protein of 25 kDa (SNAP-25)
Syntaxin-1 and synaptobrevin 2 (or vesicle-associated membrane protein VAMP2)
Sec1/Munc18-like (SM) proteins, glucose vesicles form SNARE complex. To initiate fusion, synaptobrevin 2, a vesicle (v-) SNARE fuses with the target (t-) SNAREs syntaxin-1 and SNAP-25, which are located in the target cell membrane (Figure 4) [9].
\nGlucose-stimulated insulin release from a pancreatic β-cell.
Numerous SNARE isoforms [syntaxin-1, −3 and −4, SNAP-25 and -23, synaptobrevins 2 and 3 (VAMP2 and 3)] are involved in glucose-stimulated insulin secretion whereas VAMP 8 (a non-essential SNARE protein for glucose-stimulated insulin secretion) has its role to regulate glucagon-like peptide-1-potentiated insulin secretion. In addition to SNARE and SM proteins, a calcium sensor is required to initiate membrane fusion. Synaptotagmins (highly expressed in neurons and endocrine cells) participated in Ca2+-dependent exocytosis processes. Seventeen synaptotagmins (Syts 1–17) have been identified while only eight (Syt-1, −2, −3, −5, −6, −7, −9 and −10) are able to bind Ca2+ and form a complex with the SNAREs to smooth the progress of and activate vesicle-membrane fusion process. Only Syt-3, −5, −7, −8 and −9 are concerned with insulin exocytosis [10].
\nSeveral proteins are disturbed in the insulin signaling pathways in different conditions of insulin resistance, particularly obesity, type-II diabetes mellitus, metabolic syndrome, cardiovascular diseases, inflammatory disorders, and cancer [11].
\nIt is tetramer protein, composed of 02 extracellular α- subunits and two trans membrane β-subunits. α-subunits have a binding site to insulin while the β-subunits contain an intrinsic tyrosine kinase activity towards intracellular side. Insulin binding to α-subunit leads to conformational change and activation of β-subunit which results in tyrosyl autophosphorylation of the insulin receptor. After being activated and phosphorylated, several major and better characterized insulin signaling intracellular docking proteins {Src homology collagen (SHC), associated protein substrate (APS) and insulin receptor substrates- 1 & 2 (IRS-1 and IRS-2)} binds to insulin receptor for tyrosyl phosphorylation. All these proteins activate glucose uptake and metabolism, protein synthesis, gene expression, cell survival, growth, development, and differentiation. IRS proteins are phosphorylated on various tyrosine residues of the C-terminal region and generate specific sites for binding of proteins containing Src homoly-2 (SH2) domains [phosphatidylinositol-3 kinase (PI-3 K), Nck, and Grb-2.
\nIt is composed by a catalytic subunit (p110) and a regulatory subunit (p85) and mediate metabolic effects of the insulin. Binding of p85 subunit to phosphorylated tyrosine residues of IRS proteins activate catalytic activity of p110 subunit and subsequent rise in the generation of phosphatidylinositol 3,4-bisphosphate (PIP2) and phosphatidylinositol 3,4,5-trisphosphate (PIP3) content. Downstream proteins from PI3 K pathway figure out several serine/threonine kinases e.g. phosphoinositide-dependent protein kinase-1 (PDK-1), protein kinase B (PKB/Akt), protein kinase C (PKC), p70 S6 kinase (p70S6 K) and glycogen synthase kinase-3 (GSK-3). All these kinases are involved in translocation of glucose transporter-4 (GLUT-4) from intracellular vesicles to plasma membrane, glycogen and protein synthesis, antiapoptotic effects and gene expression (Figure 4).
\nSignaling pathways which are involved in glucose uptake due to insulin induction starts with the recruitment of APS to activated insulin receptor and subsequent association and tyrosine phosphorylation of Cbl which interacts with Cbl associated protein (CAP) through an SH3 domain and with flotillin (a constituent of lipid raft, through a sorbin domain). Complex CrkII/C3G then binds to the phosphorylated tyrosine and residues of Cbl and activate C3G activity that exchanges GDP for GTP of TC10 (a small G-protein that belongs to the Rho family). After being activated, TC10 participates in GLUT-4 translocation (Figure 5) [12].
\nSummary of the main insulin signaling pathways. GLUT-1 and -4: Glucose transporter-1 and -4; Grb-2: Growth receptor binding-2; GSK-3: Glycogen synthase kinase-3; IR: Insulin receptor; IRS-1 and -2: Insulin receptor substrate-1 and -2; MAPK: Mitogen-activated protein kinase; PDK-1: Phosphoinositide-dependent kinase-1; PIP2: Phosphatidyl-inositol diphosphate; PI3: Phosphatidyl-inositol triphosphate; P: Phosphate; PKC: Protein kinase C; PP-1: Phosphoprotein phosphatase-1; p70S6K: Protein 70 S6 kinase; p90rsk: Protein 90 ribosomal S6 kinase; Shc: Src homology collagen; SHP-2: Phosphatase with Src homology 2 domain; SoS: Son of Sevenless.
This cascade starts with
The association of Shc to insulin receptor
Binding of Grb-2 to Shc or to IRS-1
Formation of the Grb-2/SoS (Son of Seven less) in the plasma membrane.
This complex leads to the activation of c-Ras and raf, starting the MAPK cascade. MAPK pathway is involved in insulin induced differentiation, cell growth, and development, along with some metabolic effects e.g. glycogen synthesis and GLUT-4 translocation to plasma membrane (Figure 4). However, this cascade is not enough or even required to this later effect [13].
\nIt occurs when insulin-sensitive tissues (skeletal muscle, adipose tissue and liver) cannot respond properly to hormones which cause several chronic diseases, particularly those which are linked to obesity (type-II diabetes mellitus, metabolic syndrome, dyslipidemias, cardiovascular diseases, cancer and neurodegenerative diseases). However precise mechanisms of insulin resistance are not fully understood. Following factor have been proposed to participate in its development;
\nAs free fatty acids are elevated in obesity and related illness, they are supposed to be responsible for insulin action impairment but still complete mechanisms are not known. More availability of long chain saturated fatty acids results leads to insulin resistance in liver, skeletal muscle and adipose tissue. Various hypotheses proposed to explain insulin resistance induced by saturated fatty acids [14] are;
Randle cycle
Oxidative stress
Modulation of gene transcription
Accumulation of intracellular lipid derivatives (diacylglycerol and ceramides)
Mitochondrial dysfunction
Inflammation
Chronic state of inflammation in insulin responsive tissues is major contributor to insulin resistance in obesity and related diseases. However, precise mechanisms as well as mediators involved in this interaction are not completely defined yet. Intracellular redox balance is delicately synchronized process that includes multiple generating pathways and degrading systems. Physiologically, ROS contribute in essential biological responses but their accumulation causes oxidative stress condition because of their highly oxidant nature to oxidize multiple intracellular components particularly membrane phospholipids, proteins, and DNA. In insulin resistance, increased ROS production and/or decreased ROS degradation is observed that leads to an oxidative stress condition and activation of signaling pathways related to stress. Oxidative stress is also responsible for muscle disorders and contributes to insulin resistance process. Transgenic mice expressing human ubiquitin protein E3 ligase (a protein that binds and promotes degradation of superoxide dismutase-1) leads to reduced superoxide degradation and as a result increased oxidative stress in the form of atrophy and sclerosis [15].
\nActivation of signaling pathways to stress is another reason of insulin resistance. Several serine/threonine kinases activated by oxidative stress pathways (JNK, PKC, GSK-3, NF-kB, and p38 MAPK) have been suggested to impair insulin signaling pathways [16].
\nExpression of genes involved in lipid and glucose metabolism, insulin signaling, inflammation, redox balance and mitochondrial function is modified in insulin signaling, which shows that these processes participate in the pathophysiology of insulin resistance. Disturbed mitochondrial function has been suggested to have a central role in these alterations, since this organelle participates in all these processes [17].
\nMany of the drugs have a combination of effects. If a person needs two or more treatments to manage glucose levels, insulin treatment may be necessary. Possible treatments for type 1 diabetes include [18]:
Metformin (Glucophage, Glumetza, others): It is generally 1st medication for type-II diabetes and works by reducing gluconeogenesis in liver and improves body’s sensitivity to insulin so that body utilizes insulin in more effective way.
Sulfonylureas: They help patients body to secrete more insulin. Its examples are glyburide (DiaBeta, Glynase), glipizide (Glucotrol) and glimepiride (Amaryl) and its possible side effects are low blood sugar and weight gain.
Meglitinides: Repaglinide (Prandin) and nateglinide (Starlix) works like sulfonylureas by stimulation of pancreas to secrete more insulin but they are faster acting with short duration of their effect in the body and have risk of causing hypoglycemia and weight gain.
Thiazolidinediones: Along with Metformin, it include rosiglitazone (Avandia) and pioglitazone (Actos). They make the body’s tissues more sensitive to insulin but these drugs causes weight gain and increased risk of heart failure and anemia that’s why, these medications generally aren’t 1st choice treatments.
DPP-4 inhibitors: Sitagliptin (Januvia), saxagliptin (Onglyza) and linagliptin (Tradjenta) are its different forms and help to lessen blood sugar levels but tend to have very unassuming effect as they do not cause weight gain but may cause joint pain and increase pancreatitis risk.
GLP-1 receptor agonists: These are injections to sluggish digestion and lower blood sugar levels. They often cause weight loss and its possible side effects are nausea and increased risk of pancreatitis. It includes Exenatide (Byetta, Bydureon), liraglutide (Victoza) and semaglutide (Ozempic). Current research has shown that liraglutide and semaglutide may reduce risk of heart attack and stroke (in people at high risk).
SGLT2 inhibitors: They prevent kidneys from reabsorbing sugar into blood and leads to its excretion via urine. It includes canagliflozin (Invokana), dapagliflozin (Farxiga) and empagliflozin (Jardiance). They may reduce the risk of heart attack and stroke in people with a high risk of these conditions while its side effects may include vaginal yeast infections, urinary tract infections, low blood pressure and a higher risk of diabetic ketoacidosis. Only Canagliflozin in this drug class has been associated with increased risk of lower limb amputation.
Insulin: People with type-II diabetes need insulin therapy. In past, insulin therapy was used as a last option but today it’s often prescribed due to its instant benefits. It’s possible side effects are low blood sugar (hypoglycemia) and its different forms are:
Rapid-acting injections: They take their effect within 5–15 minutes but last for a shorter time of 2–4 hours and include:
Insulin lispro (Humalog)
Insulin aspart (NovoLog)
Insulin glulisine (Apidra)
Short-acting injections: Its effect starts between 30 minutes to 1 hour but it last for 3–8 hours e.g.
Regular insulin (Humulin R and Novolin R)
Intermediate-acting injections: It is effective after 1–4 hours and last for 12–18 hours. e.g.
Insulin isophane, also called NPH insulin (Humulin N and Novolin N)
Long-acting injections: They are effective after 1/2 hours and last for between 14 and 24 hours. Its different forms are:
Insulin glargine (Toujeo)
Insulin detemir (Levemir)
Insulin degludec (Tresiba)
Premixed injections: These are combinations of the above types of insulin and all takes effect from 5 minutes to 1 hour and last for 10–24 hours and its different forms are:
Insulin lispro protamine and insulin lispro (Humalog Mix 50/50 and Humalog Mix 75/25)
Insulin aspart protamine and insulin aspart (NovoLog Mix 50/50 and NovoLog Mix 70/30)
NPH insulin and regular insulin (Humulin 70/30 and Novolin 70/30)
People can breathe in rapid-acting inhalable insulin which produces its effects within 12–15 minutes and lasts for 2–3 hours e.g.
Insulin human powder (Afrezza)
Non-Inulin Injectables:
For Patients with Type-1 Diabetes: These drugs are common for type 1 diabetic patients and its different forms are:
Amylin analogs: Pramlintide (Symlin) which mimics another hormone, amylin, that plays a role in glucose regulation.
Glucagon which can reverse blood sugar levels when they fall too low as a result of insulin treatment.
For patients with Type-II Diabetes:
Insulin: It can also manage high blood glucose levels in type-II diabetes but doctors typically prescribe it only when other treatments have not had the desired effect. Type-II diabetic pregnant women may also use it for the reduction of disease effects on fetus while for people with high blood glucose levels, in-spite of applying lifestyle measures to bring them down, doctors can prescribe non-insulin drugs to lower blood glucose. These drugs are:
Sulfonylureas: They improve insulin secretion by the pancreas into blood and people use following newer medicines most often because of their less adverse effects. These are:
Glimepiride (Amaryl)
Glipizide (Glucotrol)
Glyburide (DiaBeta, Micronase, Glynase)
The older, less common sulfonylureas are:
Chlorpropamide (Diabinese)
Tolazamide (Tolinase)
Tolbutamide (Orinase)
Today these drugs are less prescribed than in the past as they can cause hypoglycemia, leading to other health issues:
Meglitinides: They improves insulin secretion and might also improve the effectiveness of body to release insulin during meals. Its different forms are:
Nateglinide (Starlix)
Repaglinide (Prandin)
Biguanides: They boost the effect of insulin, reduce the amount of glucose from liver and increase uptake of blood glucose into cells.
Metformin: It is the only licensed biguanide in the US and is available in the form of Glucophage, Glucophage XR, Glumetza, Riomet, and Fortamet.
Thiazolidinediones: They reduce the resistance of tissues to the effects of insulin and are associated with serious side effects so they need monitoring for potential safety issues. People with heart failure should not use these medications. They include:
pioglitazone (Actos)
rosiglitazone (Avandia)
Alpha-glucosidase inhibitors
acarbose (Precose)
miglitol (Glyset)
Dipeptidyl peptidase inhibitors
alogliptin (Nesina)
linagliptin (Tradjenta)
sitagliptin (Januvia)
saxagliptin (Onglyza)
Sodium-glucose co-transporter 2 (SGLT2) inhibitors: They cause body to release more glucose into the urine from the bloodstream and might also lead to a modest amount of weight loss, which can be a benefit for type-II diabetic patients. These include:
canagliflozin (Invokana)
dapagliflozin (Farxiga)
empagliflozin (Jardiance)
ertugliflozin (Steglatro)
Incretin mimetics: The drugs that imitate incretin hormone and stimulate insulin release after meals are:
exenatide (Byetta, Bydureon)
liraglutide (Victoza)
dulaglutide (Trulicity)
lixisenatide (Adlyxin)
semaglutide (Ozempic)
Oral combination drugs: Drugs that are obtained after combination of some of previous drugs include:
alogliptin and metformin (Kazano)
alogliptin and pioglitazone (Oseni)
glipizide and metformin (Metaglip)
glyburide and metformin (Glucovance)
linagliptin and metformin (Jentadueto)
pioglitazone and glimepiride (Duetact)
pioglitazone and metformin (Actoplus MET, Actoplus MET XR)
repaglinide and metformin (PrandiMet)
rosiglitazone and glimepiride (Avandaryl)
rosiglitazone and metformin (Avandamet)
saxagliptin and metformin (Kombiglyze XR)
sitagliptin and metformin (Janumet and Janumet XR)
Alternatives: U.S. Food and Drug Administration has permitted ergot alkaloid, bromocriptine (Cycloset) to treat type-II diabetes. Doctors do not often propose/set down this medication. Moreover people use bile acid sequestrants to manage cholesterol levels which can also help to maintain steady blood sugar levels. Along with these, only colesevelam (Welchol) is approved for type-II diabetes.
They are used to treat high blood pressure to prevent or manage kidney complications of diabetes.
\nPeople can manage cardiovascular risks of diabetes (like heart disease and stroke) by taking them to lower cholesterol levels at a dozen of once per day on doctors recommendation.
\nIt is key part of diabetes management and prevention and doctors might suggest medicines to cure it without effective lifestyle measures [19]. These drugs are
Lorcaserin (Belviq): It enhances the feeling of being packed after food and help to treat diabetic obesity.
Orlistat (Alli and Xenical): This drug decreases absorption of fat from diet and also support weight loss.
Phentermine and topiramate (Qsymia): It is a grouped drug and reduce appetite to treat obesity.
There are many guide lines for each person’s health situation and each can choose best one according to their health conditions [20] e.g.
For people with type 2 diabetes and atherosclerotic cardiovascular disease (CVD), 2018 guidelines recommend following drugs as part of the antihyperglycemic treatment:
Sodium-glucose cotransporter 2 inhibitors (SGLT2)
Glucagon-like peptide 1 receptor agonists (GLP1-RA)
Type-II diabetic people with atherosclerotic CVD and heart failure or a high risk of heart failure should be prescribed with:
Sodium-glucose cotransporter 2 inhibitors
To treat people with type-II diabetes and chronic kidney disease, doctors urged to consider following guidelines to stop chronic kidney disease, CVD or both, from getting worse.:
Sodium-glucose co transporter 2 inhibitor
Glucagon-like peptide 1 receptor agonist
When medicines and lifestyle changes are not enough to manage diabetes, a less common treatment can become an option. Other treatments include different surgical procedures for treating type-I or type-II diabetes [21, 22, 23, 24, 25] which are as follows:
\nIt is also called weight-loss surgery or metabolic surgery and it help obese and type-II diabetic patients to lose a large amount of weight and regain normal blood glucose levels. Even some people with diabetes may no longer need their diabetes medicine after it. Efficacy of this surgery can be checked by the variations in blood glucose level, type of weight-loss surgery and the amount of lost weight by the patients. Moreover it can also be monitored by the time occurrence of diabetes and on duration of usage of insulin. Current research suggested that weight-loss surgery also may help to improve blood glucose control in obese type-I diabetic people but still scientists are finding long-term results of this in type-I and II diabetic patients [21].
\nNIDDK has leading role to develop artificial pancreas technology. Artificial pancreas replaces manual blood glucose levels by the shots or pumping of insulin. Single system monitors blood glucose levels throughout the patient’s life and provide insulin or a combination of insulin and glucagon routinely. The system can also be monitored remotely by parents or by medical staff. In 2016, FDA approved a type of artificial pancreas system, called a hybrid closed-loop system which tested blood glucose level after every 5 minutes throughout the day and night and automatically provided right amount of insulin to body. But when person still needed manual adjustment of insulin amount, pump delivered it at meal times. But artificial pancreas make patient free from some of daily tasks which are needed to keep blood glucose level steady or help to sleep through the night without need of wake and test blood glucose or to take medicine. Hybrid closed loop system was available in the U.S. in 2017. NIDDK has funded several important projects on different types of artificial pancreas devices for the better help of Type- I diabetic people for proper management of disease. These devices may also help type-II diabetic and gestational diabetic people to cure their disease [22, 23].
\nThis is an experimental treatment for poorly controlled type-I diabetes as in this condition immune system attacks islet cells. Pancreatic islet transplant replace shattered islets with new ones to make and release insulin. In this process, islets are donated from the pancreas of donor of pancreas and are transferred to a type 1 diabetic patient. As researchers are still doing work on pancreatic islet transplantation, so procedure is only accessible to volunteers of research studies [24, 25].
\nBioactive molecules from Natural products have been proved to improve insulin resistance and its associated complications by suppressing inflammatory signaling pathways [26]. Medicinal plants cannot be obsolete and still play a prominent role in human health care. Among natural sources, over 1200 plants have been claimed as antidiabetic remedies. While over 400 plants along with its 700 recipes and compounds have been scientifically evaluated for type-II diabetes. Metformin was developed on the basis of biguanide compound from an antidiabetic herb, French lilac and is now its a first-line drug against type-II diabetes. Medicinal plants also contains a diverse bioactive compounds and can have multiple actions on insulin action, insulin production, or both. With a focus on scientific studies of selected glucose-lowering herbs, phyto compounds and their ability to target insulin resistance, cell function, incretin related pathways and glucose (re)absorption (Figure 6a and b), multiple studies have been done.
\nMechanisms underlying herbal therapies using antidiabetic plants and phytocompounds. (a) Different types of medicinal herbs can be classified based on their modes of action such as insulin resistance (type 1 herbs), -cell function (type 2 herbs), and GLP-1 (type 3 herbs) and glucose (re) absorption (type 4 herbs), (b) The selected.
While more than 400 plants and compounds have shown In-vitro and/or In-vivo antidiabetic activities. Instead of listing each extract/compound, here, selected chemicals from plants and/or their extracts with the ability to control blood glucose levels as well as to modulate mechanisms involved in insulin resistance or cell function or incretin-related pathways or glucose (re)absorption can be tabulated (Table 1) along with chemical structure, antidiabetic activity and action in cells/animal models and the results of administration of the plant extracts and compounds to diabetic patients [27].
\nActive compounds and biological actions of antidiabetic herbs.
All hormones for the regulation of blood glucose levels along with their source organ up to the level of cell have been discussed in first section of chapter. Then different Pathways involved in regulating blood glucose levels in normal and abnormal conditions has been explained. Genes, Molecular and cellular targets to regulate blood glucose levels in normal and abnormal conditions has been discussed with particular focus on molecular basis of insulin signaling pathways and this pathway has been linked with Mechanism of Insulin Action and Molecular Basis of Insulin Resistance which is may be due to fatty acids, inflammation, stress and altered expression of several genes. Current scenario of Drugs and therapies to cure blood glucose regulation problems for the management of type 1 and type 2 diabetes has been explained. At the end New approaches to drug development and therapies by green synthesis to have been mentioned.
\nAll authors are highly acknowledged to the host institutions for providing a forum for the publication of this data.
\nAll authors declare that they do not have any conflict of interest with any company or organization or person.
\nAll authors are highly acknowledged to their parents and teachers who contributed their whole life for making their siblings and students a successful person.
\nMafA | musculoaponeurotic Fibrosarcoma Oncogene Family, A |
MafB | musculoaponeurotic Fibrosarcoma Oncogene Family, B |
SNPs | single Nucleotide Polymorphism |
ADCY5 | adenylate cyclase 5 |
FADS1 | fatty acid desaturase 1 |
IGF1 | insulin-Like Growth Factor 1 |
B-GPCRs | class B G protein-coupled receptors |
class A-GPCRs | class A G protein-coupled receptors |
Cbl | cannabinoid 1 |
GDP | guanosine diphosphate, |
GTP | guanosine diphosphate, |
Shc | Src homology and collagen protein |
c-Ras | rat sarcoma |
raf | rapidly Accelerated Fibrosarcoma |
JNK | c-Jun N-terminal kinase |
PKC | protein kinase C |
GSK-3 | glycogen synthase kinase-3 |
NF-kB | nuclear factor kappa-light-chain-enhancer of activated B cells |
p38 MAPK | p38 mitogen-activated protein kinases |
ACE inhibitors | acetylcholine Esterase Inhibitors |
NIDDK | National Institute of Diabetes and Digestive and Kidney Diseases |
FDA | Food and Drug Administration |
NIGMS | National Institute of General Medical Sciences |
NIDA | National Institute on Drug Abuse |
IntechOpen publishes different types of publications
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