Overall mean error calculation.
\r\n\t
",isbn:"978-1-83969-561-2",printIsbn:"978-1-83969-560-5",pdfIsbn:"978-1-83969-562-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"65f2a1fef9c804c29b18ef3ac4a35066",bookSignature:"Dr. Luis Loures",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10756.jpg",keywords:"Urban Processes, Urban Patterns, Redevelopment Strategies, Landscape, Land Transformation, Urban Models, Urban Evolution, Urban Organisation, Legislation, Sustainable Development, Green Infrastructure, Regional Planning",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 23rd 2021",dateEndSecondStepPublish:"March 22nd 2021",dateEndThirdStepPublish:"May 21st 2021",dateEndFourthStepPublish:"August 9th 2021",dateEndFifthStepPublish:"October 8th 2021",remainingDaysToSecondStep:"23 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Loures has worked on pioneering research on circular planning applied to post-industrial landscape redevelopment. Since he graduated he has published several peer-reviewed papers at the national and international levels and he has been a guest researcher and lecturer both at Michigan State University (USA) and at the University of Toronto (Canada) where he has developed part of his Ph.D. research with the Financial support from the Portuguese Foundation for Science and Technology (Ph.D. grant).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"108118",title:"Dr.",name:"Luis",middleName:null,surname:"Loures",slug:"luis-loures",fullName:"Luis Loures",profilePictureURL:"https://mts.intechopen.com/storage/users/108118/images/system/108118.png",biography:"Luís Loures is a Landscape Architect and Agronomic Engineer, Vice-President of the Polytechnic Institute of Portalegre, who holds a Ph.D. in Planning and a Post-Doc in Agronomy. Since he graduated, he has published several peer reviewed papers at the national and international levels and he has been a guest researcher and lecturer both at Michigan State University (USA), and at University of Toronto (Canada) where he has developed part of his Ph.D. research with the Financial support from the Portuguese Foundation for Science and Technology (Ph.D. grant).\nDuring his academic career he had taught in several courses in different Universities around the world, mainly regarding the fields of landscape architecture, urban and environmental planning and sustainability. Currently, he is a researcher both at VALORIZA - Research Centre for Endogenous Resource Valorization – Polytechnic Institute of Portalegre, and the CinTurs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve where he is a researcher on several financed research projects focusing several different investigation domains such as urban planning, landscape reclamation and urban redevelopment, and the use of urban planning as a tool for achieving sustainable development.",institutionString:"Polytechnic Institute of Portalegre",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"8",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Polytechnic Institute of Portalegre",institutionURL:null,country:{name:"Portugal"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"205697",firstName:"Kristina",lastName:"Kardum Cvitan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/205697/images/5186_n.jpg",email:"kristina.k@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"7476",title:"Land Use",subtitle:"Assessing the Past, Envisioning the Future",isOpenForSubmission:!1,hash:"5b0c406adac8447ffeb089e29eac8ea9",slug:"land-use-assessing-the-past-envisioning-the-future",bookSignature:"Luís Carlos Loures",coverURL:"https://cdn.intechopen.com/books/images_new/7476.jpg",editedByType:"Edited by",editors:[{id:"108118",title:"Dr.",name:"Luis",surname:"Loures",slug:"luis-loures",fullName:"Luis Loures"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8295",title:"Landscape Reclamation",subtitle:"Rising From What's Left",isOpenForSubmission:!1,hash:"1fb7d9e280708a190a90c3b352c93d45",slug:"landscape-reclamation-rising-from-what-s-left",bookSignature:"Luis Loures",coverURL:"https://cdn.intechopen.com/books/images_new/8295.jpg",editedByType:"Edited by",editors:[{id:"108118",title:"Dr.",name:"Luis",surname:"Loures",slug:"luis-loures",fullName:"Luis Loures"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5962",title:"Estuary",subtitle:null,isOpenForSubmission:!1,hash:"43058846a64b270e9167d478e966161a",slug:"estuary",bookSignature:"William Froneman",coverURL:"https://cdn.intechopen.com/books/images_new/5962.jpg",editedByType:"Edited by",editors:[{id:"109336",title:"Prof.",name:"William",surname:"Froneman",slug:"william-froneman",fullName:"William Froneman"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"314",title:"Regenerative Medicine and Tissue Engineering",subtitle:"Cells and Biomaterials",isOpenForSubmission:!1,hash:"bb67e80e480c86bb8315458012d65686",slug:"regenerative-medicine-and-tissue-engineering-cells-and-biomaterials",bookSignature:"Daniel Eberli",coverURL:"https://cdn.intechopen.com/books/images_new/314.jpg",editedByType:"Edited by",editors:[{id:"6495",title:"Dr.",name:"Daniel",surname:"Eberli",slug:"daniel-eberli",fullName:"Daniel Eberli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"71086",title:"Mathematical Modeling of Aerodynamic Heating and Pressure Distribution on a 5-Inch Hemispherical Concave Nose in Supersonic Flow",doi:"10.5772/intechopen.91041",slug:"mathematical-modeling-of-aerodynamic-heating-and-pressure-distribution-on-a-5-inch-hemispherical-con",body:'\nAerodynamic heating has been a major study issue for a long time now and many investigations have been conducted by researchers in order to achieve high-quality simulation results, both on the theoretical and the numerical level. Experimental studies of the above phenomenon are rare, as conducting such research is prohibitively expensive.
\nAerodynamic heating is a phenomenon worth studying as its significance lies in two major scientific fields. At first, aerodynamic heating is the most significant parameter of the emitted infrared radiation from aircraft, a fact that is of direct interest in military studies. According to statistics, the majority of aerial vehicles downed in modern warfare was due to weapons based on detecting Infrared signature level of those vehicles. Their percentage is as high as 89% of total aircraft shot downs between 1967 and 1993, according to Thompson J. et al. [1]. Apart from the emitted infrared radiation, the phenomenon of aerodynamic heating has to be taken into consideration when designing aerospace vehicles that fly at speeds comparable to that of sound. Ultrasonic aircraft and aircraft that re-enter the atmosphere can develop temperatures high enough that have to be carefully considered when designing the aircraft, in order to avoid unpleasant situations.
\nAerodynamic heating is a phenomenon that takes place mostly due to heat from the warm part of the boundary layer to the body’s surface. However, when approaching the speed of sound, aerodynamic heating phenomena are enhanced by the radiation emitted from molecules that are being decomposed and ionized due to the temperature rising behind the shockwave. At speeds greater than 5000 m/sec, the emitted radiation becomes significant and plays a measurable role in the phenomenon. Finally, another way that aerodynamic heating takes place concerns aircraft that fly in the higher atmospheric layers, where the distance between the air molecules is comparable to the aerial vehicle dimensions. However, the present study is focused only on the convection phenomena that lead to aerodynamic heating, as the speeds examined do not exceed Mach numbers of 2.
\nAs mentioned above, the significance of aerodynamic heating is a major issue in both the preliminary design of aerial vehicles and the calculation of the infrared radiation emitted from the vehicle in flight. Apart from experimental methods, which may offer reliable results but require enormous budgets, numerous simulations have been carried out via means of computational mechanics. Computational mechanics simulations approach the problem mainly through inviscid-viscous methods thus promising reduced computational time. AEROHEAT and INCHES are two of the simpler methods to approach the phenomenon of aerodynamic heating [2]. Both methods use the axisymmetric analog concept that allows axisymmetric boundary layer techniques to be applied to three-dimensional flows, provided that the surface streamlines are known. AEROHEAT calculates approximate surface streamlines based solely on the body geometry. INCHES uses an approximate expression for the scale factor in the windward and leeward planes, which describes the spreading of surface streamlines. An empirical relation then generates circumferential heating rates.
\nDRA Farnborough developed the SAPPHIRE software that is capable of calculating the surface temperatures and the characteristics of the flow field for a given geometry and defined flight characteristics. The above software is a series of codes that use PHOENICS [3] as the main CFD solver. The main advantage of CFD techniques is that aerodynamic heating can be related to other phenomena, as well, such as conjugating heat transfer and providing more detailed results. In order for SAPPHIRE to provide reliable results on the thermal trace, aerodynamic heating of the body has to be calculated with an error that does not exceed 5% [4]. For this purpose, a PHOENICS-based code was developed that has the possibility to validate results with available experimental data. The purpose of the present work is to make the model more time efficient and more accurate, producing results closer to the available experimental data.
\nAccording to finite volume or codes with FEM, attempts have been made, with acceptable results, by means of conjugating CFD and FEM. Murakami et al. [5] used CFD coding for the Navier-Stokes equations to calculate the outer flow, while the finite element method was used to perform the thermal analysis via equations describing unsteady heat conduction for a 3D field. The results were impressive, as they were very close to the experimental data measured in a wind tunnel experiment. Lu Jianwei and Wang Qiang [6] in order to calculate the emitted IR radiation suggested a division of the computational grid into two regions: the fluid and the solid. The fluid region is modeled by the Navier-Stokes equations, while turbulence is modeled by the RNG k-ε model. In the solid region, the energy transfer equation is used. In a similar study for calculating the emitted IR radiation, Marlene Johansson and Mats Dalenbring [7] developed the SIGGE software. SIGGE uses inputs of temperature, pressure, and species distribution that have been roughly estimated beforehand, together with the grid of the geometry being studied. The Navier-Stokes equations are solved by the VOLSOL solver, while turbulence is described by the k-ε model.
\nApart from CFD methods, attempts have been made to simulate phenomena that involve aerodynamic heating via other methods as well. Rodrigo C. Palharini and Wilson F. N. Santos [8] used the Direct Simulation Monte Carlo (DSMC) method to simulate a two-dimensional steady supersonic flow in an orthogonal cavity. Main interests of this study were the characteristics of the aerodynamic surface, such as heat transfer coefficients, etc. Another method introduced by Georgia Tech Research Institute [9] named GTSIG (Georgia Tech Signature Inviscid Model) involves the calculation of temperature distribution by creating thermal meshes and partial differential equations. Finally, Christopher J. Riley et al., in their study [2], developed a 3D technique without viscous calculations that has the ability to calculate the degree of surface heating. Surface streamlines are calculated from the inviscid solution and the axisymmetric analog. Then, they are used in combination with an equation for convective heat transfer, to calculate the total surface heat transfer. The results of this method are satisfying as they are close to experimental data and the results of both the Navier-Stokes and the VSL equation solutions.
\nIn order to describe the phenomena under investigation, use is made of the conservation equations describing a three-dimensional, single phase, turbulent, steady state flow. The above equations are formulated in such a manner as to incorporate the physical modeling of the case study under the following assumptions:
One phase, steady-state flow
Compressible fluid
Newtonian fluid
Absence of chemical reaction
It is also clear that because of the general geometries considered, the equations must be modified in a way to be suitable for use in a body-fitted coordinate (BFC) system.
\nAssuming that \n
Furthermore,
\nAssume a non-orthogonal coordinate system, defined as \n
where Uj are the Cartesian components and the unit tangent vectors alongside the three directions are:
\nwhere \n
In order to obtain the resolute [10] of the momentum equation in each of the coordinate directions, we form the dot product of tangent unit vectors with the momentum equation (Eq. (1)). The momentum equation, considering the assumptions and the physics of this simulation, is derived as follows:
\nwhere
\nIn order to obtain the resolute of the momentum equation in each of the coordinate directions, the dot product of the tangent unit vector and the above momentum equation is formed:
\nwhere \n
From vector calculus, using the identity \n
Also,
\nThus,
\nForming the dot product of the above equation and (Eq. (7)), we have:
\nAlso, for a scalar variable φn:
\nand
\nUsing Eqs. (11)–(13), we can write (Eq. (6)), as follows:
\nand
\nFrom Eqs. (1) and (3), the partial derivative in the direction ξn is:
\nWith inverse
\nwhere Gn,j the elements of the inverse matrix of the elements gn,j.
\nSince the case study is for steady flow, the conservation of mass equation is hence simplified in the following form for the Cartesian coordinate system:
\nUsing body-fitted coordinates, the continuity equation must be solved in terms of the velocity components along the coordinate directions. These components, un, would be \n
Velocity components in terms of the resolutes are
\nwhere
\nwhere
\nand
\nUsing Eqs. (1) and (18), the following expression for the velocity is derived:
\nwhere
\nthe vector used to obtain the resolutes (projections) of the velocity vector \n
From Eqs. (1), (16), and (20):
\nFinally, the continuity equation can be written as \n
In order to evaluate the use of different turbulence models in the cases under consideration, a parametric study was accomplished by trying three “popular” turbulence models: (i) standard k-ε model, (ii) RNG k-ε model, and (iii) k-ω model. The k-ε model simulation was chosen in order to reproduce the original simulation accomplished by Steve Rooks [4]. In order to take it one step further and include the effect of the swirling of the flow, another simulation was performed using the RNG k-ω model. Last but not least, the k-ω turbulence model was used as well, as preliminary studies proved its suitability for wall-bounded flows.
\nBy generalizing Eq. (14), the appropriate equation for any scalar variable can be provided [11]:
\nwhere
\nand φ a general scalar variable like H (enthalpy), k, ε, etc. Regarding k (turbulence kinetic energy) and ε (turbulence dissipation rate):
\nwhere ui, uj the velocity components in the direction of the Cartesian coordinates xi, xj with i = 1,2,3 and j = 1,2,3 [10]. It must also be noted that the effective exchange coefficients for k, ε are as follows:
\nwhere μl is the laminar viscosity; C1, C2, and Cμ are turbulence model constants; and σk,t and σε,t are the turbulence Prandtl numbers for k, ε [10].
\nIn total seven differential equations are solved, namely, the continuity equation, the three momentum equations, the enthalpy equation, and the two equations for the turbulence variables.
\nSpecial attention was given to the treatment of the near-wall region, as it is of high importance for the type of flows studied and the 2-equation models used to simulate turbulence do not, in general, produce results of high accuracy in near-wall regions. The near-wall region was treated via a log-law wall function that also played a major role in deciding the computational grid as well as ensuring computational economy since there is no need to solve throughout the viscous sub-layer. The use of this method was introduced by Launder and Spalding [12], and the formula used is:
\nwhere
\nU: the absolute velocity parallel to the wall in the first node of the grid
\nUτ: the friction velocity
\nk: the Von Karman constant
\nE: the surface’s roughness parameter
\nY+: the dimensionless distance from the wall
\nThe above function is applied to nodes whose dimensionless distance from the wall is between 30 and 130. In this region of the boundary layer, the effects of turbulence and viscosity are equally important.
\nBecause of the selection of body-fitted coordinates to design the grid of the problem, the velocity must be broken down to its components and transferred from the Cartesian coordinate system to the BFC used in the present study.
\nAt entrance, a uniform flow is applied of Mach 2 speed. The rate of flow is consistently calculated for each control volume. The kinetic energy of turbulence and its rate of dissipation are considered constant with values of 21.78 m2/s2 (10% of the mean velocity squared) and 2.765*105 m2/s3 (assuming turbulence viscosity 1000 times the laminar value), respectively (\nFigure 1\n).
\nThe plane of inlet boundary conditions specification.
The outlet boundary conditions describe the fact that there is no alteration in the rate of mass flow, as there is neither mass creation nor destruction. As for the turbulence kinetic energy, turbulence dissipation rate, and the enthalpy, Neumann conditions prevail, suggesting there is no alteration of values regarding these properties along the boundary (\nFigure 2\n). Since the boundary conditions must be transported in the BFC system, the Jacobian of the transformation used is:
\nThe plane of outlet boundary conditions specification.
and the general form of the functions:
\nFinally, the boundary condition at the northern boundary is prescribed external pressure. Like the boundary conditions at the outlet, the kinetic energy of turbulence, its dissipation rate, and the enthalpy are described by Neumann boundary conditions, implying absence of factors capable of altering their values along the northern boundary.
\nThe study for grid independence came down to the same results as the one proposed by Steve Rooks [4]. The computational grid used is one with dimensions 48*45*1 and has been proved to lead to grid-independent results [4]. Briefly, the grid divides the computational field in three regions along the x-axis and three along the y-axis, as one can see in the following figure (\nFigure 3\n).
\nThe computational grid.
Along the y-axis the three sub-regions correspond to the area below the lip, the area of the lip, and finally the area above the lip. A most important role in the dimensions of the grid played the fact that a denser or a sparser grid would result in improper use of the wall functions, as it would affect the dimensionless distance Y+.
\nIn order to solve the equations, the finite-volume method is employed. As for regular grids, the finite-volume equations can be derived by integrating over a control volume in a system with body-fitted coordinates. The use is made of the Gauss theorem that transforms a volume integral to a surface one:
\nwhere \n
Any dependent variable’s partial differential equation is represented by a coupled set of algebraic equations of the following form:
\nwhere the Ai’s represent the influence of convection and diffusion, Sφ is the source term, and P refers to the control volume under consideration. Σi indicates the summation over the neighboring nodes. All the above equations are derived through work that cannot be presented here due to limited space [10].
\nThe numerical simulations in this work are realized by the fluid dynamics package PHOENICS. The SIMPLEST algorithm is used in the iterative procedure, whereas the equations are solved via the finite-volume method. The momentum equations are solved using the initial estimate of the pressure field, using velocities that do satisfy momentum but not in the general continuity. For each cell, the equation of (inlet-outlet) is formed, and an equation of pressure correction is solved where the coefficients are A (d(vel))/dp and the sources are the errors in continuity. Afterwards, the pressure field is corrected alongside with the velocity field. The iterative procedure continues until the errors in the equations of momentum and continuity are acceptable.
\nIn order to improve convergence and eliminate great fluctuations in values of variables in consecutive iterations that could lead to the divergence of the solution, relaxation techniques are used. In the present study, two techniques are used: the linear relaxation and the false time-step relaxation. Linear relaxation is used for the variables of pressure, enthalpy, turbulence kinetic energy, and turbulence dissipation rate as it is more suitable for scalar variables in the following form: φnew = φold + α(φnew − φold). In the case of the vector variables of U1 and V1, false time-step relaxation is used as it is more suitable for the velocity variables in the following form ρVp/δt(Φ* − Φρ) = β(Φ* − Φρ) where VP is the cell volume, Φ* the previous iteration value of variable Φ, Φρ the value of φ in the last iteration, and δtfalse the false time step, generally of the order of the cell residence time.
\nIn the original coding, the Hybrid discretization scheme was used. However, in order to investigate further the most efficient way to simulate the phenomenon, runs were also carried out using the upwind discretization scheme that, according to literature, is more efficient when describing directional flows but introduces more “false-diffusion” errors.
\nIn order to improve the simulation results both in terms of convergence speed and agreement with the experimental data, numerous modifications were made to the original coding. These modifications include the calculation of the diffusion coefficient by means of the harmonic mean and not the numerical mean, and the calculation of reference kinematic viscosity for laminar flow was accomplished via Sutherland’s law. Moreover, the initial values of the variables for enthalpy and velocity were not chosen randomly, as in the original coding but were calculated based on the simulation’s data so as to help the convergence procedure with more “realistic” initial variable values. At the northern boundary, a boundary condition that suggests constant mass flow was used so as to ignore the effects of diffusion. Finally, in opposition to the initial coding, where false time-step relaxation was used for the variables of enthalpy, turbulence kinetic energy, and turbulence dissipation rate, in the modified version, for these variables, linear under-relaxation was used.
\nIn terms of this present research, many simulations were carried out, and results of the most relevant eight of them were brought into comparison in order to decide which method is more efficient in simulating the phenomenon studied by Markley et al. [13]. The first simulation carried out in terms of this research was the reproduction of the simulation carried out by Steve Rooks [4] in an up-to-date version of the PHOENICS. The other seven include the modified original version, described in the above paragraph, although no alteration in either the turbulence model or the discretization scheme was made. The other six were the combinations of the three turbulence models (k-ε, RNG k-ε, and k-ω) with the two discretization schemes (Hybrid and Upwind) studied in this research paper.
\nIn order to examine the validity of the results of the simulation, a comparison had to be made with the available experimental data. The available experimental data are for temperature, as that was the main point of interest at the experiment conducted by NACA [13], measured behind the lip, and high enough so as the flow would be unaffected by boundary layer phenomena. As a result, measurements were taken along the line Y = 18 and behind the region dictated by cell X = 20.
\nThe first simulation carried out in terms of this research was the reproduction of the simulation carried out in [4] using our modified version of the PHOENICS code, so as comparison can be carried out. The modifications made to the original coding that led to improved results, both in terms of agreement with the experimental data and in terms of decreased convergence time, include the calculation of the diffusion coefficient by means of the harmonic mean rather than the numerical mean and the calculation of reference kinematic viscosity for laminar flow that was accomplished via Sutherland’s law. Moreover, the initial values of the variables for enthalpy and velocity were not chosen randomly, as in the original coding, but were calculated based on the simulation’s data so as to help the convergence procedure with more “realistic” initial variable values. At the northern boundary, a boundary condition that suggests constant mass flow was used so as to ignore the effects of diffusion. Finally, different to the initial coding, where false time-step relaxation was used for the variables of enthalpy, turbulence kinetic energy, and turbulence dissipation rate, in the modified version, for these variables, linear relaxation was used.
\nFor this initial modified version, no alterations were made as far as the turbulence modeling and the discretization scheme are concerned. The initial modeling with k-ε turbulence modeling and the Hybrid discretization scheme were used. As a result of the above changes, the convergence of the coding was faster by 48.33% as convergence was reached at 5427 iterations instead of 10,503 for the same level of errors remaining, and there were even slight improvements in the agreement with the experimental results as well.
\nThe following diagram depicts the distribution of temperature along the x-axis above the airfoil at the height of the line Y = 18, as mentioned above (\nFigure 4\n).
\nComparison diagram for temperature distribution along the x-axis.
As seen in the above diagram, the two simulations differ only slightly, and it can be noted that the modified version offers a marginal 0.04% better accuracy along with the faster convergence. More precisely, at the point where the temperature drop is noticed, which is the lip region, the accuracy is improved by 0.015%, whereas after the threshold of 5.3 cm, which is behind the lip region, the accuracy is improved by 0.27%.
\nLikewise, the diagrams depicting the main velocity and density distribution along the x-axis slightly differ from the diagrams of the initial simulation since the mean fault is 0.5847 and 0.9%, respectively (\nFigures 5\n and \n6\n).
\nComparison diagram for velocity distribution along the x-axis.
Comparison diagram for density distribution along the x-axis.
Regarding the distributions of temperature, velocity and density along the Y-axis, defined by the line X = 20 (\nFigures 7\n–\n9\n), no major difference is noticeable in their main part, as one can notice from \nFigures 7\n–\n9\n. The mean errors are 0.7, 3.06, and 0.9%, respectively. Yet, reaching the northern boundary, the results from the modified simulation differ from the initial one, as they do not alter the value they have settled in following the exit of the boundary layer. In the initial simulation, an increase can be spotted for the variables of temperature and density and a decrease in the value of velocity. The increase in the temperature value along with the decrease in velocity can be explained from the fact that enthalpy is assumed constant. However, in the modified simulation, the steady mass flow boundary condition neglects diffusion phenomena, and therefore there is stability in this variable. This approaches the nature of the original experiment, as no factors able to alter the values of these variables exist near the northern boundary.
\nComparison diagram for temperature distribution along the y-axis.
Velocity distribution along Y-axis.
Density distribution along Y-axis.
\n\nFigures 10\n–\n13\n present the contour plots for temperature, density, and velocity distributions as well as the vector diagram of the velocity vectors.
\nContour plot of the temperature distribution on the computational field for the modified simulation (k-ε hybrid).
Contour plot of the velocity distribution on the computational field for the modified simulation (k-ε hybrid).
Contour plot with the density distribution on the computational field for the modified simulation (k-ε hybrid).
Velocity vectors zoomed in the area of interest depicting the recirculations.
In order to investigate further the problem and any possible aspects that may improve the quality of the results produced, a parametric study was conducted. The parameters studied were both the turbulence model and the discretization scheme. Simulations were conducted using all three different turbulence models: k-ε, RNG k-ε, and k-ω. Each of the above turbulence models was used in combination with either the Hybrid discretization scheme or the Upwind.
\nIn total, six different simulations were performed. The results were compared to the available experimental data in order to evaluate their accuracy. The schematic of the comparison can be seen in the diagram that follows (\nFigure 14\n).
\nComparison diagram of parametric simulations and experimental data.
The simulation results can also be seen in numbers in the following table, where the mean error is calculated (\nTable 1\n).
\n\n | k-ε Hybrid | \nk-ε Upwind | \nRNG k-ε Hybrid | \nRNG k-ε Upwind | \nk-ω Hybrid | \nk-ω Upwind | \n
---|---|---|---|---|---|---|
Overall mean error (%) | \n4.9529 | \n3.5055 | \n4.9729 | \n3.5741 | \n5.034 | \n3.3418 | \n
Overall mean error calculation.
As one can see, the most successful simulation in terms of the overall mean error is the one engaging the k-ω turbulence model in combination with the Upwind discretization scheme. If we take a closer look, it is obvious that it is the only simulation that seems to approach the steep temperature descent in the 4–5 cm region. In that region, all simulations fail to keep the mean error below 5%; however, as one can see in the following table, the k-ω Upwind simulation achieves a mean error of 5.6274% (\nTable 2\n).
\n\n | k-ε Hybrid | \nk-ε Upwind | \nRNG k-ε Hybrid | \nRNG k-ε Upwind | \nk-ω Hybrid | \nk-ω Upwind | \n
---|---|---|---|---|---|---|
Mean error in region 4–5 cm (%) | \n8.42 | \n6.763 | \n8.4331 | \n6.8564 | \n8.0097 | \n5.6274 | \n
Mean error calculation in the 4–5 cm region.
The difficulty in simulating realistically the behind the lip region is possibly due to heat conduction phenomena. In the simulations performed, no solution is run for conjugate heat transfer, and given that the airfoil geometry is very thin, heat conduction taking place on the edge of the lip is speculated to be one among the causes that lead to higher calculated temperatures than the experimental data.
\nWhen examining the region behind the lip, where the flow presents a more stabilized form than the one when encountering the lip, the turbulence model that achieves better accuracy is the RNG k-ε model with the Upwind discretization scheme. This can be seen in both the diagram and the table that follow (\nFigure 15\n and \nTable 3\n).
\nComparison diagram between simulation performed and experimental data in the behind the lip region.
\n | k-ε Hybrid | \nk-ε Upwind | \nRNG k-ε Hybrid | \nRNG k-ε Upwind | \nk-ω Hybrid | \nk-ω Upwind | \n
---|---|---|---|---|---|---|
Mean error in behind the lip region (%) | \n6.5465 | \n0.3348 | \n6.6621 | \n0.6050 | \n8.0915 | \n1.9930 | \n
Mean error calculation in behind the lip region.
It must be noted that in all simulations performed, the discretization scheme that produced the best results was the Upwind scheme. That would be justified by the fact that the flow is basically unidirectional and is characterized by high velocity. As a result, the diffusion phenomena are of very low intensity, with the convective phenomena being the main terms in the discretization equation. In both k-ω and k-ε simulations ran, the benefits from altering the initial code were still valid.
\nThe calculation of aerodynamic heating phenomena includes many aspects that can interfere with both the speed of convergence but the accuracy of the results as well. Apart from the choice of proper initial value and other numeric factors, attention has to be paid to the flow field characteristics in order to decide the suitability of the turbulence model and the discretization scheme that will be used in order to achieve better results. Despite the fact that improvements for the 4–5 cm region were also suggested here, future research could focus on simulating the lip region, as this study’s main interest lied in the behind the lip region. In order to achieve that, high-order discretization schemes could be used, like the Van Leer scheme as well as high-order turbulence models, like the large eddy simulation (LES) and the direct numerical simulation (DNS) or any combinations.
\nHeat transfer and pressure measurement on a 5-inch hemispherical concave nose at a Mach number of 2.0 is investigated via the use of PHOENICS to evaluate aerodynamic heating phenomena. In continuation of [4], the code is modified in order to achieve better results both in terms of faster convergence but also in terms of more accurate results as well. Finite-volume method is used, while turbulence models and discretization schemes are studied as parameters in order to optimize results. Before altering the above mentioned parameters, attempts were made to improve the speed of convergence. Altering the values of input variables in order to be closer to the problem considered as well as choosing relaxation methods suitable for each variable type, scalar or vector, along with a few other changes described above, managed to improve the convergence speed by up 48.33%. Moreover, an improvement in boundary conditions of the northern boundary so as to ignore dissipation phenomena corrected the results of all variables at the northern region of the computational field, as their values were stabilized, as expected. In the parametric study, the main observation was that the discretization scheme plays a major role in the quality of the results, as the flow is unidirectional and is characterized by high speed of 2.0 Mach. As a result, dissipation phenomena are not comparable to convection, which plays the major role. The choice of the Upwind discretization scheme seems to be more accurate, as it considers the fact that the flow is unidirectional and provides results improved by more than 1.5% for given turbulence model on the whole computational field. That improvement can go up to 6% when discussing regions behind the lip, where the flow has been stabilized.
\nThe parametric study regarding the turbulence model produced interesting results, as well. In order to evaluate solely the impact of the turbulence model, the discretization scheme used was the Hybrid, as in the original. As it appears, there is not only one fitting turbulence model for this simulation, as their suitability changes with regions. When studying the flow field in the lip region (4–5 cm), the k-ω turbulence model gave the best results, improved by 0.5% when compared to the original simulation performed with k-ε turbulence model. The improvement introduced by the use of the k-ω model in that area is due to the fact that this model can calculate better the effect of the created vortices. When both k-ω turbulence model and Upwind discretization scheme were implied, the total improvement in that area of interest went up to almost 3% (5.6274% discrepancy from the experimental data, compared to 8.42%). However, the study showed that behind the lip (x > 5.2), where flow has been stabilized, the use of the RNG k-ε turbulence model combined with the Upwind discretization scheme improved the mean error by more than 6% (0.3348% compared to 6.5465).
\nWe would first like to thank our colleagues in DRA Farnborough, who performed the original step in this modeling effort. Their inspired research was the basis of this current research paper. Moreover, we would also like to thank Dr. Mike Malin, Technical Support Manager of CHAM Ltd., whose assistance was truly helpful to go over technicalities that appeared along the way of this present research.
\nPHOENICS is licensed by CHAM Ltd., London, UK. The present model may become available from the present authors.
\nA pesticide is any substance which is used to prevent, destroy or repel any pest from causing any damage. The term pest represents any living organism that may cause harm to human in respect to food competition, destruction of property and spread of disease. Pests include insects, rodents, microbes, fungi and weeds (unwanted plants), etc. of agricultural, medical and veterinary importance, and therefore, a pesticide can be an insecticide, an insect and plant growth regulator, a fungicide, an herbicide, a molluscicide, and an algaecide, etc. based on the target pest organism.
The major site of action for most pesticides are the nervous and endocrine systems and, therefore, are also potentially toxic to human with serious direct or indirect adverse health effects. Human beings are exposed to pesticides directly or indirectly. Direct exposure occurs during pesticide application process in agriculture, public health and livestock, and fumigation while indirect exposure involves ingestion of contaminated food and water, and inhalation of pesticides droplets from the drift. Children are more susceptible to pesticides than adults due to their physical makeup, behavior and physiology, and exposure to very low levels at early developmental stages can cause adverse health effects. Codex Alimentarius committee and the Pesticide Data Program of the United States Department of Agriculture have established pesticide maximum residue limits in edible food which must be followed to avoid any health risks.
Pesticide exposures have been linked to the elevated incidence of human diseases such as cancers, Alzheimer, Parkinson, amyotrophic lateral sclerosis, asthma, bronchitis, infertility, birth defects, attention deficit hyperactivity disorder, autism, diabetes, and obesity, respiratory diseases, organ diseases and system failures. People who are exposed to pesticides are at a greater risk to develop various cancers including non-Hodgkin lymphoma (NHL), leukemia, brain tumors, and cancers of the breast, prostate, lung, stomach, colorectal, liver, and the urinary bladder.
Pesticides cause genetic and epigenetic changes by involving various processes at cellular levels. Pesticides may be involved in endocrine disruption and induction of inflammatory signals which result in production of reactive oxygen species (ROS) causing oxidative stress. ROS disrupt the cellular functions of mitochondria and endoplasmic reticulum.
This chapter covers different types, importance and modes of action of pesticides. Human exposure to pesticides and pesticide residues in food are also discussed. Finally, the impacts of pesticide exposure on human health with focus on the major chronic health effects (neurotoxic, genotoxic and carcinogenic, and reproductive effects) and recent findings regarding health effects associated with exposure to common types of pesticides, i.e., organochlorines, organophosphates, carbamates, pyrethroids and neonicotinoids insecticides, fungicides and herbicides are discussed.
Pesticides can be classified based on chemical classes, functional groups, mode of action, and toxicity. The active ingredients of most pesticides are either organic (contain carbon) or inorganic (minerals e.g. copper sulfate, ferrous sulfate, copper, lime, sulfur, etc.). Organic pesticides are hydrophobic and more complex than those of inorganic pesticides. Organic pesticides can be natural (produced from naturally available sources) or synthetic (artificially produced by chemical synthesis in factories). The major types of pesticides used in agriculture, forestry, landscape, medical and veterinary sectors are listed in Table 1.
Type of pesticide | Active ingredient | Target pests |
---|---|---|
Insecticides | Natural and synthetic | Insect (6-legged) pests of agricultural, forestry, landscape, medical and veterinary importance |
Miticides/acaricides | Natural and synthetic | Mites (8-legged) pests of agricultural, forest, landscape, medical and veterinary importance |
Fungicides | Natural and synthetic | Fungal diseases (molds, mildews, rust) of agricultural, forestry and landscape importance |
Herbicides | Natural and synthetic | Unwanted plants (weeds) of agricultural and landscape importance |
Insect growth regulators | Synthetic | Disrupt the growth and reproduction of insect pests. IGR are species or genus specific. |
Pheromones | Natural and synthetic | Attract and trap male insects and are often species-specific. |
Plant growth regulators | Synthetic | Alter plants growth, e.g., induce or delay flowering |
Algaecides | Natural and synthetic | Algae growing on different surfaces, e.g., patios |
Molluscicides | Natural and synthetic | Slugs and snails of agricultural, forestry and landscape importance |
Biopesticides | Natural | Can be insecticides, fungicides or herbicides |
Antimicrobials | Synthetic | Microbes (mostly bacteria) of medical and veterinary importance |
Rodenticides | Natural and synthetic | Rodents (mice, rats) in agriculture, landscape, building, storages and hospitals |
Treated seeds | Synthetic | Seeds coated with an insecticide or fungicide or both to prevent damage from soil insect pests and fungus diseases |
Wood preservatives | Synthetic | Pesticides to protect wood from insect pests, fungus and other diseases |
Minimum risk pesticides | Natural and synthetic | Any pesticides which have been proven safe for human and are exempt from registration by any regulatory authorities |
Major types of pesticides used in agriculture, forestry, landscape, medical and veterinary sectors. (adopted from: National Pesticides Information Center at http://npic.orst.edu/ingred/ptype/index.html).
Pesticides are sold as formulated products. Pesticide formulations are a combination of one or more active ingredients (a.i.) and several inert ingredients. Active ingredients control the pests. The inert ingredients help in solubility and stability of the product. A ULV (Ultra Low Volume) formulation need specialized spray equipment and the Ready-to-Use formulations are already diluted and are appropriate for indoor or small areas, for example, aerosols (A), granules (G), and most baits (B) [1].
Most liquid formulations are diluted with water according to the label directions. The three main types of liquid formulations are solutions, suspensions, and emulsions. A true solution is a mixture that cannot be separated by a filter or other mechanical means while a suspension is an even mixture of very small solid particles throughout a liquid and an emulsion is a mixture of droplets of one liquid in another liquid. Common Liquid Formulations are Emulsifiable Concentrate (E or EC), Solutions (S, CS), Emulsions in Water (EW), Flowables (F, L, or SC), Microencapsulated Pesticides (M or ME) and Aerosol (A).
In dry formulations the active ingredient is on the surface of a solid carrier, such as talc, clay, or ground corncobs. Common solid formulations include Granules (G), Wettable Powders (WP or W), Soluble Powder (SP or S), Water-Dispersible Granules (WDG) or Dry Flowables (DF Water-Soluble Bags/Packages (WSB) and Baits (B).
The United Nations population division estimates 9.7 billion people by the year 2050 and to feed them, the Food and Agriculture Organization (FAO) of the United Nations estimates that an 80% increase in food production is necessary. This increase in production will come from an increase in yields of crops as well as a decrease of damage to crops due to pests. There are approximately 9000 species of insects/mites (14% loss), 50,000 species of plant pathogens (13% loss) and 8000 weeds species (13% loss) worldwide [2]. Without pesticide application the pest losses to fruits, vegetables and cereals would reach 78%, 54% and 32%, respectively. Pesticides are, therefore, indispensable in agricultural production and there will be a need for pesticide based pest control and food security in the future. Pesticides are also used to control vector-born infectious diseases such as Zika virus, Lyme disease, and rabies, household pests like cockroaches, bed bugs, and as repellents etc. More than 1000 active ingredients are used in pesticides around the world to ensure food safety and prevention from pests and the highest amount (~45%) is spent on herbicides followed by insecticides, fungicides, and other types of pesticides.
Human beings get exposed to pesticides either actively through occupational exposure or passively through non-occupational exposure. Pesticides occupational exposure may occur during manufacturing, transportation, sale, and application process including exterminators. For example, in an incident of occupational exposure, 2800 workers were poisoned during malathion spray for malaria vector control in Pakistan [3]. Parents working in agriculture industry usually take pesticide contaminated clothing, equipment home, which has been associated with the development of cancers in their children.
Non-occupational exposure may include pesticides residues ingestion with contaminated food and water and inhalation of pesticides droplets from the air through drift from point of release or fumigation. Human beings are also exposed to residual indoor sprays and outdoor fogging of insecticides applied against insect pests of public health importance and homeowners exposed to structural pest control pesticides. Additionally, treatment of ectoparasites in pets, e.g. fleas, is also a source of exposure, especially for children.
Exposure through the intact skin (dermal exposure) is the most common route and may occur as a result of a splash, spill, or spray drift, during mixing, loading, disposing, and/or cleaning of application equipment especially when proper protective equipment are not used. Dermal absorption can be influenced by the amount/concentration, duration of exposure and temperature/humidity. Absorption is high through groin areas, the eyes and ear canal. Liquid formulations (e.g., emulsifiable concentrates) are readily absorbed through the skin compared to the solid formulations (e.g., powders, dusts, and granules).
Accidental ingestion of pesticides (oral exposure) occurs by drinking from unlabeled containers when pesticides are stored in food/drink container, water stored in pesticide-contaminated bottles, eating or smoking while, or after handling pesticides or through application equipment or pesticide residues in food and water. Inhalation of pesticides (respiratory exposure) may occur due to application of fumigants (which change into toxic gas after coming in contact with moisture in air) or presence of fine droplets in air (particle or vapor drift) after application of pesticides. Pesticides can enter blood stream after absorption through lungs.
Pesticides are distributed throughout the human body through the bloodstream and are excreted through urine, skin, and exhaled into air after metabolism. These pathways also determine the toxicity of any pesticide. Pesticides recognized as persistent organic pollutant (POP) are fat soluble and are easily accumulated within the human fat-tissues, breast milk, and maternal blood placenta.
The amount of risk from pesticide exposure depends on the toxicity and the exposure to the pesticide. Toxicity is a measure of how harmful or poisonous a pesticide is (causing sickness or other unwanted effects), while exposure is a measure of the contact (duration) with a pesticide. Toxicity of a pesticide is measured as lethal dose (LD50). The LD50 value is the statistical estimate of a pesticide (mg/kg of body weight) which will kill 50% of the test animals within a stated period of time (24 hours to 7 days). The LD50 value also depends on the route of entry of a pesticide; oral LD50 for oral ingestion, dermal LD50 for skin contact exposure and Lethal Concentration (LC50) for inhalation of fumigants and pesticide vapors.
A short term exposure or exposure to a single dose will cause acute toxicity with its health effects. Chronic toxicity results from repeated exposure to a pesticide over a longer period of time from several months to years. Hazard symbols, signal words and color on the primary display panel of a pesticide label are based on their dermal toxicity.
Insecticides Resistance Action Committee (IRAC) has classified insecticides into 32 groups based on their mode/site of action, in addition, there are 5 other types of insecticides with unknown modes of action. Most commonly used insecticides work at different sites in the nervous system of insects. Insecticides target the same sites of action in human nervous system and cause toxicity with adverse health effects. Carbamate (group 1A) and Organophosphate (OP) (group 1B) insecticides inhibit the enzyme Acetyl Choline Esterase (AChE) and cause hyper-excitation. AChE terminates the action of the excitatory neurotransmitter acetylcholine at the nerve synapses. Examples of pesticides inhibiting AChE include dichlorvos, malathion, phorate, carbaryl, carbofuran, etc. Cyclodiene organochlorine insecticides (OC) (group 2A) and phenylpyrazoles (group 2B) block the gamma amino butyric acid (GABA)-activated chloride channel causing hyper-excitation and convulsions. GABA is the major inhibitory neurotransmitter in insects. Examples of insecticides inhibiting GABA include endosulfan and fipronil. Synthetic pyrethroids and natural pyrethrins (group 3A) and DDT (group 3B) keep sodium channels open causing hyper-excitation and, in some cases, nerve blockage. Sodium channels are involved in the propagation of action potentials along nerve axons. Examples include deltamethrin and permethrin. Neonicotinoid insecticides (group 4A) bind to the acetylcholine site on nicotinic acetylcholine receptor (nAChRs) causing a range of symptoms from hyper-excitation to lethargy and paralysis. Examples include acetamiprid, clothianidin, imidacloprid, thiacloprid and thiamethoxam. Other groups of insecticides that work on nervous system includes those which allosterically activate nAChRs (e.g. spinetoram, spinosad) or glutamate-gated chloride channels (GluCls) (e.g. abamectin, emamectin benzoate), or allosterically inhibit the GABA-activated chloride channel and cause paralysis (e.g. broflanilide and fluxametamide). Glutamate is an important inhibitory neurotransmitter in insects. Other insecticides will block the nAChR ion channel or sodium channels, e.g. indoxacarb, cause nervous system shutdown and paralysis.
Fungicides inhibit fungal growth by interfering with critical cellular processes. Fungicide resistance action committee (FRAC) classify fungicides and bactericides into 50 groups based on the site of action. Within each group, there are target sites, which are the specific enzymes to which the fungicides bind. The different known target sites include nucleic acids metabolism, cytoskeleton and motor protein, respiration, amino acids and protein synthesis, signal transduction, lipid synthesis or transport/membrane integrity or function, sterol biosynthesis in membranes, cell wall biosynthesis, melanin synthesis in cell wall and host plant defense induction. Some fungicides and herbicides are considered endocrine disrupting pesticides.
Herbicides are pesticides that inhibit or interrupt normal plant growth and development. Herbicides are widely used in agriculture, landscape industry, and non-crop areas for weed management. Herbicides resistance action committee (HRAC) has classified herbicides into 27 groups. These include: growth regulators (synthetic auxins; auxin transport inhibitors), seedling growth inhibitors, photosynthetic inhibitors, amino acid synthesis inhibitors, lipid synthesis inhibitors, cell membrane disrupters, pigment inhibitors.
Growth regulator herbicides consist of the synthetic auxin and auxin transport inhibitory compounds and the most commonly used synthetic auxins include 2,4-
‘Pesticide residue’ means any specified substance in food, agricultural commodities, or animal feed resulting from the use of pesticides. The term also includes any derivatives of a pesticide, such as conversion products, metabolites, reaction products, and impurities considered to be toxic. Application of pesticides during the production or storage of agricultural commodities result in pesticide residues in food (fruits, vegetables, grain, meat, etc). Pesticide residues are also found in the drinking water. Pesticide residues can build up to harmful levels through bio-accumulation and bio-magnification within the food chain.
WHO, in collaboration with FAO performs pesticide risk assessment to humans, both through direct exposure and through residues in food. The WHO core assessment group on pesticide residues review toxicological data and establish the acceptable daily intakes (ADIs) and acute reference doses (ARfDs) of pesticide residues for different commodities through a lifetime of food consumption. The ADIs are amount of pesticide residues which will not result in adverse health effects. Codex Alimentarius Commission (the intergovernmental standards-setting body for food) establishes maximum residue limits (MRLs) for pesticides in food based on ADIs.
The MRL depends on the crop it is used on, and the same pesticide active ingredient may have different MRL values when used on different crops. Extraneous maximum residue limit (EMRL) refers to the maximum permitted limit of residues of mostly POP pesticides, which were previously used as pesticides but not registered any more, and residues arising from environmental contamination (including previous agricultural use) or residues from uses of these pesticides other than for agricultural purpose, e.g. DDT, Aldrin, etc.
There are several reports of pesticide residues detected on food exceeding the MRL values. For example, in India, vegetable samples were tested for the presence of OC, OP and pyrethroid insecticides, and 15.3% samples exceeded the MRL. In two Brazilian pesticide residue monitoring programs less than 3% of the samples had residue levels above the MRL. Pesticide residues were detected in 34% of samples of cereal grains collected throughout Poland and 3% samples contained residues over the maximum limit. A study from Maule Region (Talca, Chile) found pesticide residues on the fruits and vegetables schoolchildren brought as snack [5].
The pesticide residues detected in fruits and vegetables from Lithuania had multiple pesticides; 9 residues in grapes and tea, 5-9 residues in orange, mandarins, lemons, peaches, pears and 3-5 residues in pomegranates, plums, cucumbers, tomatoes and strawberries, and found that 2.6% samples exceeded the MRL values [6]. In a European Union study 14–23% of the samples had detectable residues of more than one active ingredient where 3.0–5.5% samples had residues levels above the MRL [7]. Exposure to multiple pesticide residues could be due to intake from a single food item containing multiple residues or from several food items each containing one or more residues. The combined toxic effects of two or more compounds can be independent, additive or synergistic.
Both recreational and medicinal cannabis samples contained high levels of residual pesticides and pesticides not legally allowed to be used on cannabis products in Oregon. Medicinal cannabis products were found to have mean levels of residual pesticides that were 3-12 times higher than recreational products, and 9 of the 50 pesticides identified were classified highly or extremely hazardous by the WHO [8].
Pesticide residues have been found in surface, groundwater and potable water samples from India [9]. Pesticide residues levels in river water and in drinking water samples in Turkey were significantly high compared with guideline values set by Turkey, EU and WHO as hazardous to human health [10]. Higher concentrations of pesticides in ambient air were recorded from potato farm sites in Prince Edward Island, Canada, Taihu Lake region of China and Kaweah Reservoir, CA, USA. A total of 87 pesticides were identified in the household dust samples from the rural Yakima Valley of Washington state, 47 of these have evidence of neurotoxicity included in the EPA list [11].
The short-term acute adverse effects pesticide exposure on human health are stinging eyes, rashes, blisters, skin irritations, blindness, nausea, dizziness, diarrhea and death. Exposure to pesticides in agricultural work can cause serious risks to the respiratory system causing chronic cough, dyspnea, wheezing and expectoration, decreased lung capacity, asthma, and bronchitis. These respiratory problems were found in workers in flower crops in Ethiopia, coffee plantations in Brazil and banana plantations in Costa Rica. In banana farming in Rio Grande do Norte (Brazil), the use of pesticides was related to the symptoms of burning in the throat and lungs, airway congestion, cramps, skin peeling, diarrhea, headache, chest pain, weakness, cough and skin irritation.
In banana production region of the Ribeira Valley (Brazil), workers (majority males, low schooling, mean age 39.6 years and 13.8 years of working time) had moderate obstructive disorder (10.0%) and mild obstructive disorder (13.3%) with decreased FEV1 (forced expiratory volume in 1 second) and FEV1/FVC (the ratio between forced expiratory volume in the first second and forced vital capacity and is very important for the detection of obstructive disorders). Similarly, exposures to mixtures (pollutants and pesticides) in children with asthma in California were also associated with reduced lung function measures FEV1 and FVC [12].
Many studies have found positive associations with pesticide exposure and children’s respiratory and allergic effects such as asthma, wheezing, coughs, acute respiratory infections, hay fever, rhinitis, eczema, chronic phlegm, and lung function impairments. A study of school-age children with asthma in the agricultural community of Yakima Valley (Washington State) found that increase in exposures to OP insecticides was related with increase in LTE4 levels which was associated with a higher risk of asthma morbidity [13]. The neonicotinoid insecticides (e.g. imidacloprid, nitenpyram) are nicotinic receptors agonists and their exposure cause nausea, vomiting, muscle weakness, respiratory effects, headache, lethargy, and tachycardia.
The long-term chronic adverse effects of pesticides exposure are cancers, birth defects, reproductive harm, neurological and developmental toxicity, immunotoxicity, and disruption of the endocrine system. The chronic effects of pesticides on human can be categorized into three major groups; neurotoxic effects, genotoxic and carcinogenic effects, and reproductive effects.
Neurotoxicity can be defined as any adverse effect on the central or peripheral nervous system caused by chemical, biological or physical agents. A developing nervous system in children (during replication, migration, differentiation, myelination of neurons, and synapse formation) is more susceptible to neurotoxic chemicals including pesticides. Chemicals (pesticides) can cause neuronal cell death by disruption of the cytoskeleton, induction of oxidative stress, calcium overload, or by damaging mitochondria. Most of the synthetic insecticides, some fungicides and herbicides, currently in use are neurotoxicants.
Pesticide molecules are small and lipophilic in nature, and can enter from blood to brain and then in neurons, glial cells and brain micro vessels. Pesticides can disrupt blood-brain barrier receptors in the central nervous system which enhance chronic toxicity and affect the receptor-mediated transcytosis. Neuronal cells are more susceptible to oxidative stress due to their high polyunsaturated fat content in the myelin sheaths, low anti-oxidative capabilities, enzymatic systems with transient metals that aid in the production of free radicals, and demand for high oxygen and glucose metabolism rate.
OPs and carbamates bind to and phosphorylate/carbamalate the AChE which causes accumulation of acetylcholine at cholinergic synapses causing overstimulation of muscarinic and nicotinic cholinergic receptors. Neuropsychiatric disorders, such as anxiety and depression, are observed in patients with acute and long-term poisoning from OPs. OPs may also cause an intermediate syndrome and OP-induced delayed polyneuropathy (OPIDP) 1-3 weeks after a single exposure. In carbamates, the AChE inhibition is reversible and acute intoxication is generally resolved within a few hours.
The OP insecticides can disturb the function of mitochondria by inducing oxidative stress in central nervous system through critical depletion of mitochondrial energy, the activation of proteolytic enzymes, and DNA fragmentation leading to apoptosis. The dysfunction of mitochondria and oxidative stress is responsible for several neurological diseases, including Parkinson’s disease, seizure, cognitive dysfunction, attention and memory deficits, dementia, depression, and Alzheimer’s disease. OP triggered induction of a xanthine oxidase may play a role in cognitive impairment.
In a study, increased inhibition of cholinesterase enzyme with increased exposure to OP insecticides was confirmed in both occupationally exposed (OE) and environmentally exposed (EE) groups of people. The OP exposure, mainly in the EE group, was associated with a diminished neuropsychological performance; general mental status, language, memory, attention, executive function, praxis and psychomotricity.
Acute poisoning due to exposure to OP (particularly chlorpyrifos) was reported with higher prevalence of peripheral polyneuropathy, and deterioration of cognitive functions (verbal fluency, and visual and auditory memory) was observed in agricultural workers and in inhabitants of rural agricultural areas. Exposure to OP insecticides in rural schoolchildren was associated with a lower processing speed in children and an IQ lower than expected for their age.
Exposure to type I pyrethroids cause tremor syndrome (behavioral arousal, aggressive sparring, increased startle response, and fine body tremor progressing to whole-body tremor, and prostration) while type II pyrethroids exposure cause salivation syndrome (profuse salivation, coarse tremor progressing to choreoatetosis, and clonic seizure). The poisoned cerebral cortex affect learning, memory, emotions, and movement. Pyrethroids exposure has been positively associated with hearing loss in U.S. adolescents. Pyrethroids exposure induced Tau protein malfunction which may be the mechanism underlying cognitive impairment. Paraquat, triazine and pyrazole (herbicides) through oxidative stress, raised influx of calcium and the stimulation of nitrogen oxide species, and aggravated Aβ amyloidogenesis cause cognitive impairment.
Exposure to endocrine disrupting chemicals (EDCs) including many pesticides can disrupt maternal thyroid imbalance which can result in permanent and lifelong neurodevelopmental consequences for their children, including attention-deficit disorder, autism spectrum disorder, and cognitive and behavioral dysfunction. Workers of fruit and seed export companies in a rural area of Santiago exposed to methyl bromide (CH3Br, a fumigant) had increased concentration of CH3Br in blood after application which resulted in a higher frequency of insomnia, headaches, paresthesias, mood swings, memory loss, and decreased concentration [14].
Parkinson’s disease (PD) is characterized by progressive degeneration of dopaminergic neurons of the nigrostriatal pathway and the formation of alpha-synuclein (α-syn)-containing Lewy bodies. Dieldrin (OC) is selectively toxic to dopaminergic cells, disrupts striatal dopamine activity, and may promote α-syn aggregation while ziram (dithiocarbamate fungicide) increases the probability of synaptic vesicle release by dysregulation of the ubiquitin signaling system and increases excitability in both aminergic and glutamatergic neurons leading to PD.
A genotoxic agent can be a physical, chemical or biological agent that can interact with the genetic material (DNA) causing alterations, damage or ruptures, and those that interfere with enzymatic processes of repair, genesis or polymerization of proteins involved in chromosome segregation. These alterations could lead to impaired embryonic development or be the initial steps in the development of cancer. Pesticides exposure can cause genomic damage. Genetic damage caused by pesticides is broadly classified into three classes; (i) Pre-mutagenic damage like DNA strand breaks and DNA adducts (ii) gene mutations like insertion, deletion, inversion and translocation (iii) chromosomal aberrations, including loss or gain of whole chromosome (aneuploidy), deletion or breaks (clastogenicity), and chromosomal rearrangements.
Farmers exposed to pesticide mixtures in Greece had possible clastogenic (chromosome breakage cause mutation) and aneugenic (abnormal number of chromosomes) effect of pesticides on the genetic material. DNA methylation changes in the placenta were significantly associated with the maternal plasma concentrations of OCs in early pregnancy causing prenatal toxicity. OPs affect DNA methylation, induce the AChE gene expression and activate the NMDA glutamate receptors resulting in calcium influx in the post-synaptic neurons leading to degeneration.
Genetic damage has been reported from exposure to malathion (OP), carbofuran (carbamate), triflumuron (Insect growth regulator), imidacloprid, acetamiprid and thiamethoxam (neonicotinoid insecticides), pentachlorophenol (OC), Emamectin benzoate (used in agriculture, household, and veterinary medicine), and tembotrione (novel post-emergence herbicide) (Table 2).
WHO Hazard Class | Band color | Signal word | Dermal LD50 (mg/Kg) | |
---|---|---|---|---|
Solid formulation | Liquid formulation | |||
Class Ia Extremely Hazardous | Red | VERY TOXIC | <10 | <40 |
Class Ib Highly Hazardous | Red | TOXIC | 10–100 | 40–400 |
Class II Moderately Hazardous | Yellow | HARMFUL | 100-1000 | 400-4000 |
Class III Slightly Hazardous | Blue | CAUTION | >1000 | >4000 |
Class U Products unlikely to present a hazard | Green |
Pesticides hazard classification by FAO.
Cancer is characterized by an uncontrolled cell growth with limitless replication, resistance to apoptosis, alteration of growth factors (GFs), resistance to chemotherapy, metastasis and angiogenesis. Cancer develops as a result of multi-factorial complex interactions of genetic and lifestyle factors including, diet, stress, physical and biological agents, infections, and exposure to the hazardous chemical substances. Pesticides exposure acts as a stimulant to cancer and chronic low-dose is considered one of the important risk factors for the increasing cancer incidence. Table 3 presents a list of pesticides suggesting carcinogenicity in different types of studies.
Type of cancer | ToP | Name of pesticide | Type of study | Reference |
---|---|---|---|---|
Non-Hodgkin lymphoma (NHL)and Hodgkin lymphoma (HL) | OC | P,p’-DDT | Case control | [15] |
P,p’-DDE | Agricultural health | [16] | ||
HCH | Case control | |||
MoC | Nonachlor/trans-nonachlor hexachlorobenzene | Blood Agricultural health | ||
OC | Mirex | Case control | ||
Chlordane | Case control | |||
Lindane | Case control | |||
OP | Malathion | Case control | [17] | |
Diazinon | ||||
Terbufos | Case control | [18] | ||
Dimethoate chlorpyrifos | Agricultural health | [15] | ||
PYR | Permethrin | Case control | [16] | |
NPYR | Pyrethrum | Agricultural health | [17] | |
PHE | 2,4-D | Case control | [19] | |
Mecoprop | Epidemiological | [20] | ||
CHL | Dichlorprop | Case control | [21] | |
BNZ | Dicamba | Case control | [20] | |
GLY | Glyphosate | Case control | [16] | |
Breast | OC | Pp\'-DDT | Histopathology | [22] |
Pp\'-DDD | Histopathology | [23] | ||
P,p′-DDE | ||||
β-HCH | Histopathology | [24] | ||
Heptachlor | ||||
Hexachlorobenzene | ||||
OP | Chlorpyrifos | MCF-7 breast cancer cells | [25] | |
Malathion | ||||
Terbufos | Case control/MCF-7/MCF-10F | [26] | ||
Diazinon | ||||
Dimethoate | ||||
PYR | Flucythrinate | AutoDock Vina 1.1.1 | [27] | |
Fluvalinate | ||||
Bifenthrin | ||||
Cyhalothrin | ||||
Cypermethrin | ||||
NEO | Thiacloprid imidacloprid | Hs578t cells | [28] | |
PTH | Captan | Agricultural health | [29] | |
GLY | Glyphosate | Case control | [30] | |
Prostate | OC | Pp\'-DDT Lindane | Case-control | [31] |
Endosulfan | Human prostate cancer PC3 and DU145 cell | [32] | ||
OB | Methyl bromide | Agricultural health | [33] | |
OP | Chlorpyrifos | Prostate epithelial lines | [34] | |
Dimethoate | Agricultural health | [35] | ||
Malathion Carbaryl | Case-control | [31] | ||
PYR | λ-Cyhalothrin | Prostate epithelial lines | [34] | |
Bifenthrin | PC3 human | [36] | ||
Deltamethrin | Prostate cancer cell | [37] | ||
QUI | Dichlone | Case control | [31] | |
IMI | Prochloraz | PC-3 prostate cancer cells | [38] | |
DIC | Vinclozolin | |||
MoV | M2 | |||
CHL | 2,4-D | Case control | [31] | |
2,4-DB | Histopathology | [39] | ||
2,4,5-T | ||||
CHP | Picloram | Histopathology | [39] | |
ORG | Cacodylic acid | Case control | [31] | |
TRI | Simazine Atrazine | RM1 cells | [40] | |
Mo2 | 2, 4-dichlorophenol (DCP) | Case control | [31] | |
MoD | Dinoseb amine | |||
GLY | Glyphosate | Prostate epithelial lines | [34] | |
Lung cancer | OP | Diazinon | Epidemiological | [41] |
PYT | Cypermethrin | Lewis lung cancer cells | [42] | |
αCH | Acetochlor | Agricultural health | [43] | |
TRI | Atrazine | |||
Bladder | IMZ | Imazethapyr imazaquin | Agricultural health | [44] |
Hepatocellular carcinoma | OC | Pp\'-DDT | Serum levels | [45] |
Pp\'-DDE | Toxicological | [46] | ||
OC | Endosulfan | Human liver carcinoma cells (HepG2) | [47] | |
CAR | Carbaryl | Toxicological | [46] | |
BEZ | Fluopyram | Female rat | [48]] | |
BED | Carbendazim | Toxicological | [46] | |
BEN | Dicamba | Agricultural health | [49] | |
αCH | Acetochlor | Human liver carcinoma cells (HepG2) | [47] | |
Stomach | TRI | Atrazine | Agricultural health | [50] |
Thyroid | OP | Malathion | Agricultural health | [51] |
TRZ | Penconazole | Agricultural health | [52] | |
TRI | Atrazine | Agricultural health | [53] | |
Amitrole | Nthy-ori-3-1 cell | [54] | ||
Ovarian | OC | Pp\'-DDT | Blood | [55] |
Pp\'-DDE | ||||
β-HCH | ||||
Endosulfan | ||||
OP | Diazinon | Agricultural health | [51] | |
PYR | λ-Cyhalothrin | BG-1 ovarian cancer cells | [56] | |
Cypermethrin | ||||
Cyprodinil | ||||
HYD | Fenhexamid | Mouse model with transplanted BG-1 cells | [56] | |
Colorectal | OC | Pp\'-DDE | [57] | |
Endosulfan | ||||
OP | Chlorpyrifos | Human colorectal adenocarcinoma H508 cells | [58] | |
CAR | Aldicarb | |||
αCH | Acetochlor | Agricultural health | [51] | |
Brain | OP | Dichlorvos | Male albino Wistar rats | [59] |
List of Pesticides Suggesting Carcinogenicity in different types of studies.
ToP, type of pesticide; OC, organochlorine insecticide; MoC, metabolites of chlordane; OP, organophosphate insecticide; PYT, pyrethroid insecticide; NPYT, natural pyrethroid insecticide; PHC, phenoxy-carboxylate herbicide; CHL, chlorophenoxy herbicide; BEN, benzoate herbicide; GLY, glycine herbicide; NEO, neonicotinoid insecticide; PHT, phthalimide fungicide; OB, organobromine insecticide; QUI, quinone algicide; IMI, imidazole fungicide; DIC, dicarboximide fungicide; MoV, metabolite of vinclozolin; CHP, chlorinated pyridine herbicide; ORG, organoarsenic herbicide; TRI, triazine herbicides; Mo2, metabolite of 2,4-D; Mod, metabolite of dinoseb dinitrophenol herbicide; αCH, α-chloroacetamides herbicide; IMZ, imidazolinones herbicides; CAR, carbamate insecticide/nematicide; BEZ, benzamide, pyramide fungicide; BED, benzimidazole fungicide; TRZ, triazole fungicide; HYD, hydroxyanilides fungicides.
Non-Hodgkin lymphoma (NHL) is a diverse group malignancies and its incidence has increased worldwide. Patients with immune dysfunction are at a high risk to develop NHL. Studies have reported an elevated risk of NHL with exposure to several classes of pesticides. Terbufos (OP nematicide), dimethoate, malathion and chlorpyrifos (OP insecticide), and 2,4-D and dichlorprop (chlorophenoxy herbicides) have been associated with significant risk of developing HL.
Leukemia has been associated with occupational exposure with a higher risk in livestock farmers and golf course superintendents. The risk of chronic myelocytic leukemia (CML) and acute myeloblastic leukemia (AML) was found to be higher in women. Children whose parents used garden and indoor insecticides, or whose mothers had been exposed while pregnant had increased rates of all types of leukemia. Children living on farms and those exposed to household pesticides have increased risk of leukemia. Association between occupational exposure to pesticides and chronic lymphocytic leukemia (CLL) has been reported from Spain. A nationwide study in France showed a moderate increase in incidence of childhood AL in municipalities where viticulture is common.
Brain tumors are the most common solid tumors in children and the leading cause of cancer-related mortality during childhood. A positive association has been reported between parental occupational, prenatal or residential exposure, living on a farm, mothers living on farms, rural activity and childhood brain tumors. Increased risk for primitive neuroectodermal tumors (PNETs) was associated with maternal exposure living on pig or poultry farms. Exposure to pyrethroid formulations used to control mosquitoes and cockroaches at home also increase the risk of brain tumors.
Breast cancer is the leading cause of cancer-related deaths among women. About 650 pesticides out of the 800 used worldwide can affect the functioning of the endocrine system and are called endocrine disrupting pesticides (EDPs). EDPs have the potential ability to act as tumor promoters and increasing risk of breast cancer. All women diagnosed with breast cancer between 1995 and 2005 in the city of Arica (geographic area that received massive aerial applications of malathion in 1980) were 5.7 times more likely to suffer from breast cancer compared to women diagnosed during the same period in the city of Iquique, Chile [14]. Several chemical classes of insecticides, fungicides and herbicides have been associated with breast cancer in women (Table 3).
Prostate cancer is the second most common cancer in men globally, and accounts for 7% of all cancers. More than 95% of cases of prostate cancer are androgen-dependent. The higher incidence of prostate cancer, at least in part, has been associated with the hormone disrupting pesticides and consistent positive associations between prostate cancer and pesticide exposure have been reported.
Hepatocellular carcinoma (HCC) is the 6th most common cancer, and the 4th most common cause of cancer-related mortality. The major risk factors include hepatitis B virus (HBV), hepatitis C virus (HCV), alcohol, aflatoxin contaminated foods, obesity, smoking and type 2 diabetes besides pesticides. Pesticides exposure has been associated with increased risk of developing HCC.
EDCs are emerging as one of the leading risks and are recognized as serious and urgent threats to public health. In laboratory studies, EDCs are reported to shorten gestation, alter intrauterine growth, and disrupt metabolic programming. Prenatal exposure to EDCs can affect fetal neurodevelopment through disruption of peroxisome proliferator activated receptors, mainly estrogen receptors, and thyroid hormone receptors.
Failure of testosterone production in Leydig cells leads to failure of testosterone-bound androgen receptor-mediated gene transcription necessary for spermatogenesis. Many studies have shown that various pesticides decrease testosterone levels. Testosterone is required for the final stages of sperm maturation, so a decrease in intra-testicular testosterone is likely to impair fertility. Vinclozolin (fungicide) and chlorpyrifos (OP) can reduce testosterone production. Exposure to higher concentrations of OP and dialkyl phosphates (metabolites of OPs), p,p’-DDE, fenvalerate and atrazine (chlorotriazine herbicide) have been consistently associated with lower semen quality (sperm concentration, motility, and morphology).
A study of male children from a village of cashew plantations, where endosulfan (OC, EDC) had been aerially sprayed for more than 20 years, showed a delay in sexual maturity and an alteration in sex hormone synthesis. Endosulfan, in exposed mothers, can move via trans-placental route and breast feeding to children. Exposure during critical periods of development might contribute to decline conception rates and increased incidence of female reproductive disorders, such as altered cyclicity, endometriosis, fetal growth retardation, and pregnancy loss [60].
A high incidence of spontaneous abortions 81.02 / 1000 live newborns was reported in Valparaíso Region (agricultural area) compared to 9.5 /1000 live newborns in the rest of Chile. A 28% incidence of congenital malformations in live newborns was reported in the O’Higgins Region (agricultural area) compared to only 15% of cases in non-agricultural in Chile [14].
Pesticides are used in managing pests of agricultural and public health importance, and their use will continue in future because of food security and vector control. Additionally, pesticides are used at home in fumigation for structural pests and to mitigate household pest using aerosols or sprays. It is difficult to eliminate pesticides in the near future, but they should be used with care and caution. Most pesticides are potentially toxic to human beings resulting in severe health consequences including cancers.
Epidemiological evidence suggests that there is an increased incidence of different diseases including leukemia, lymphoma, and several other types of cancers in farmers, and those who are associated with application of pesticides. There is also evidence that parental exposure, as well as, exposure in early life or adolescence could increase the longer-term risks.
Since animal studies are problematic, expensive and often generate ethical problems, cell cultures are increasingly used as a model of research. Correctly conducted and properly selected, the cell culture is an excellent experimental model reflecting human exposure to different xenobiotics through all relevant routes. The cell cultures are also becoming more widely used to study the effect of pesticides on the human body at a molecular level, which is necessary to understand the hazards and determine the level of exposure.
Some pesticides (OCs) are no longer used worldwide due to their persistence and toxicity. However, their residues or metabolites are still found in food and water samples. The use of OPs and carbamate insecticides has been reduced since the arrival of newer chemistries in different parts of the world but most of them are still use around the world.
The workplace safety standards and proper pesticide management and storage must be implemented to reduce the risks posed to human health. Pesticide users should be aware of their risks and proper handling, as well as must use personal protective equipment which are effective in reducing damage to human health. To ensure healthy childhood growth, efforts should be made to develop comprehensive pesticides risk mitigation strategies and interventions to reduce children’s exposure.
It is critical to achieve sustainable development in agricultural systems. Newer approaches in pest management have been developed which should be encouraged. For example, RNA interference- (RNAi-) based pesticides are emerging as a promising new biorational control strategy [61] and steam treatment at temperature of 150.56°C can kill 93.99% of nematode 97.49% of bacteria [62].
Future research need in the context of minimizing the impact on human health due to exposure to pesticides include an urgent need to eliminate the use of carcinogenic pesticides and to develop environmentally sound integrated pest management (IPM) strategies that use the minimum amount of pesticides. Such IPM strategies should aim at reducing the pesticides residues on food products and pesticides-free water and air.
The author acknowledges the financial support by the Sultan Qaboos University, Muscat, Oman. This work was funded through an Internal Grant # IG/AGR/CROP/18/02.
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