Modeling setting and parameterization options.
\r\n\tThe Biomechatronic book will cover all health-related areas of mechatronic systems with emphasis on medical and health-related areas of mechatronic system components. The book will generally include the following areas: Biomechanics applications, Biomaterial systems, Mechatronic systems, Sensor systems, Control systems, Actuator systems.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"2a8b3299bd359d430bc9b5bfc54f9cdf",bookSignature:"Dr. Sezgin Ersoy and Dr. Ishak Ertugrul",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10029.jpg",keywords:"Biosensors, Mechatronics, Biomechatronics, Biosystem, Control, Control system, Bioactuators, Intelligent orthosis prosthesis, Implants, Upper and lower limb rehabilitation robots, Biomechanics applications",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 14th 2019",dateEndSecondStepPublish:"November 4th 2019",dateEndThirdStepPublish:"January 3rd 2020",dateEndFourthStepPublish:"March 23rd 2020",dateEndFifthStepPublish:"May 22nd 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"156004",title:"Associate Prof.",name:"Sezgin",middleName:null,surname:"Ersoy",slug:"sezgin-ersoy",fullName:"Sezgin Ersoy",profilePictureURL:"https://mts.intechopen.com/storage/users/156004/images/system/156004.png",biography:"Sezgin Ersoy is an Associate Professor of Mechatronics Engineering and Material Science. After graduating from Marmara University, he became a faculty member at the same university. His publications include a variety of efforts to understand changes in automotive mechatronics, polymer science and biomedical technologies. He was granted fellowship at the TUBİTAK at Bourgogne University ISAT and spent one year as a visiting fellow there to study several projects between 2014 through 2015. He is the author of chapter Science Education in a Rapidly Changing World, USA 2011, and the author in Acoustic Properties of Bio Materials, Stuttgart, 2010. 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He is currently working as an Assistant Professor at Muş Alparslan University.",institutionString:"Marmara University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Marmara University",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"287827",firstName:"Gordan",lastName:"Tot",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/287827/images/8493_n.png",email:"gordan@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. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"63440",title:"Evaluating Cooling Tower Scheme and Mechanical Drag Coefficient Formulation in High-Resolution Regional Model",doi:"10.5772/intechopen.80522",slug:"evaluating-cooling-tower-scheme-and-mechanical-drag-coefficient-formulation-in-high-resolution-regio",body:'\nThe world’s population is coming increasingly urbanized, and most of this additional urbanization occurs in developing countries [1]. The land-use change and the anthropogenic heat release induced by urbanization have been recognized as important factors that have serious impacts on climate at regional scales [2, 3, 4]. There is plenty of evidence that the regional climate effect of urban is significant [5, 6, 7, 8, 9]. And urban impacts are becoming more and more important in fine weather forecasting. It is difficult to distinguish the impact of land-use change and artificial heat emissions on regional climate in the observation. But numerical model can be used to solve this problem [10]. Feng et al. [11] employed WRF coupled with single-layer urban canopy model (UCM) to investigate urban land-use change and anthropogenic heat release on regional climate in China and indicated that impact of anthropogenic heat release is larger than urban land-use change.
\nAnthropogenic heat is one contributor to the urban heat islands which destroyed the near-surface inversion and increased the stratification instability [12]. Anthropogenic heat release is the extra heat flux which can change the surface energy balance [13, 14]. Energy consumption from buildings is an important part of anthropogenic heat release that may modify near-surface energy balance [15].
\nSailor [16] provides a historical perspective on the development of models of urban energy consumption and anthropogenic impacts on the urban energy balance. It indicated that there is a positive feedback cycle that higher temperatures result from greater amounts of energy used for air cooling in most urban area [17]. Global modeling results indicated that heat release from building is the largest contributor (89–96%) to the large-scale urban consumption of energy [12]. Future climate experiments by GCMs show anthropogenic heat flux can cause annual-mean warming of 0.4–0.9°C over large industrialized regions although global-mean anthropogenic heat flux is small [18]. The temperature increased by anthropogenic heat not only depends on the amount of heat released but also on orographical factors [19]. The amount of heat released at night is lower than at day, but the temperature increase is nearly three times greater [20]. Global model shows that the extra heat from energy consumption over the 86 major metropolitan can cause up to 1° of warming in winter seasons [21].
\nRecent regional modeling results show that anthropogenic heat flux from building has a significant impact on temperature simulation on urban area [22]. The heat release of air condition caused about 1–2°C warming in summer commercial area [23]. The study of Pairs also indicated that about 0.5°C results from anthropogenic heat release and points out the air condition makes important contributions to surface warming [24]. A study on three major urban agglomerations of China suggests that contribution of anthropogenic heat release to warming is larger than the land-use change [11].
\nBut the performance of current urban canopy model is not satisfied for artificial heat emissions in urban area [11, 25]. Both UCM and BEM take the anthropogenic heat as extra surface sensible flux and can only recognize the diurnal variation [26]. But UCM and BEM cannot describe the energy exchange between anthropogenic heat flux and the urban boundary layer sensible heat flux which leads to energy balance in the boundary layer destroyed. It is noteworthy that anthropogenic heat release includes not only sensible heat flux but also latent heat flux. Anthropologic latent heat flux from urban area is ignored by UCM and BEM. Building energy model that can accurately describe energy balance mechanism of urban boundary layer and includes anthropogenic heat release urgently needs development.
\nThe development of BEM model has solved the problem to a great extent [27]. Current BEM model is capable of describing (1) the heat diffusion through walls, roofs, and floors, (2) the natural ventilation and the radiation exchanged between the indoor surfaces, (3) the heat from occupants and equipment, and (4) the energy consumption from air condition.
\nAlthough BEM has the ability to simulate the building energy-exchange process as mentioned above, the performance is not satisfied enough especially for the high-resolution forecast in urban area [3, 25, 28]. Air condition releasing is treated as sensible heat flux to potential temperature equation when couples to BEP and regional model [29]. Simulated temperature in urban area is always obviously higher than the observation by current WRF/BEM model. Assessment report in Beijing shows that latent heat flux maximum simulated by WRF/BEM is only 40 W·m−2 while the observation is about 230 W·m−2 [25]. Errors of simulated heat flux directly lead to underestimate the humidity and further affect the performance of rainfall. In most commercial buildings, anthropogenic heat can be associated to heat release from air conditioning systems. Most air conditioning systems use evaporative cooling that releases a mixture of sensible and latent heat to the environment [30].
\nMany studies show that the energy consumption of air condition from building is gradually increasing as the frequency of heat wave is increasing [31]. And heat release from the building air condition system is one of the primary sources of anthropologic latent heat flux in urban area [29, 32, 33, 34, 35]. Although heat released by air condition is considered in the current WRF/BEM, the performance is still not satisfied. Previous studies have indicated that heat released by air condition in some megacities is equal to or more than half of the surface sensible flux [23]. Simulation results show that contribution of heat released by air condition to summer warming can exceed 1° in the megacities [23, 36, 37], and contribution to nighttime temperature can reach 2° [29].
\nHowever, most air condition systems use evaporative cooling that releases a mixture of sensible and latent heat to the environment. In summer, 50–80% of their heat released by evaporative-cooled systems is in the form of latent heating [38]. In China, metropolitan electricity consumption report shows that most important energy consumption in building comes from air condition system whether in commercial or residential area [39]. Air condition usage report about Chinese metropolitan indicated that the ratio of sensible and latent heat flux by different types of air condition emissions is 20 and 80%, respectively [39].
\nSo how to correctly describe the latent heat flux released by air condition in high-resolution model is an urgent problem to solve. It has shown that a BEM coupled with a cooling tower model can improve the model performance of temperature [30]. Cooling tower scheme will obviously improve model performance of the energy exchange ability between building and its surroundings and urban boundary layer balance.
\nBeijing power consumption is gradually increasing from 1978 to 2015, and the proportion of electricity consumed by residents is also gradually increasing (Beijing Municipal Bureau of Statistics website). It is noteworthy that heat released from building air condition is the important component of summer electricity consumed by residents. Namely, heat flux released by air condition system in Beijing’s urban area becomes one of the primary sources of summer anthropologic heat as other big cities. In order to modify the simulation of heat flux released by air condition, a new cooling tower scheme [30] was coupled to rapid-refresh multi-scale analysis and prediction (RMAPS).
\nParameterizations using the specific input parameters describe the complex arrangement of buildings and streets in an urban environment. However, simulations using this type of data reproduce fine-resolution features that are not clearly reproducible by traditional methods [10, 29].
\nIn order to represent the effects of horizontal and vertical building surfaces of momentum, heat, and turbulent kinetic energy (TKE) equations, the building effect parameterization and the building energy model (BEP + BEM) [40] have been introduced. In the relevant equations, new terms about frictional and drag forces on the mean flow and the increase of the TKE between buildings are introduced. This scheme assumes drag coefficient is a constant. That is inaccurate because the magnitude of the drag is decided by building density in highly heterogeneous urban environments. A new formulation has been implemented in the BEP + BEM system to calculate the values of the drag coefficient based on the building plan-area fraction to improve the airflow simulation in the urban boundary layer. The performance of this drag formulation has been evaluated in an idealized urban configuration using computational fluid dynamical (CFD) simulations [27].
\nOur aims are as follows:
To improve the performance of BEM using the cooling tower scheme and drag formulation.
To evaluate the forecast performance of the improved RMAPS coupled with improved BEM in summer Beijing
Details of the data and the experimental design are given in Section 2. The performance of improved RMAPS is evaluated in Section 3. We summarize the findings and discuss our results in Section 4.
\nThe surface temperature and humidity data were obtained from 294 meteorological stations operated by the Beijing Meteorological Bureau (Figure 1). Vertical temperature data was gathered from a radiometer located at 39.8°N, 116.46°E. Heat flux data used in this study were obtained from the Beijing meteorological tower (39.97°N, 116.37°E), which is 325 m high and located in North Beijing.
\nHigh-resolution land-use map in model. Black dots show the 294 weather stations. The white triangle shows the location of a 325 m meteorological tower (39.97°N, 116.37°E); the blue triangle shows the location of the Naojiao station (39.8°N, 116.46°E); and the yellow triangle shows the location of the Yanqing station (40.45°N,115.97°E).
This study used operational rapid-refresh multi-scale analysis and prediction system (RMAPS) based on modified version of the WRF model (ARW versions 3.5.1), and its data assimilation system (WRFDA v3.5) was developed by the Institute of Urban Meteorology, China Meteorological Administration, Beijing [25]. The system starts with ECMWF global forecasts (at 3-h intervals) and terminates with hourly weather forecasts. Initial conditions is adjusted by WRFDA-ingested including S/C band weather radar, ground-based global positioning system meteorology (GPS-MET), radiosonde, Aircraft Meteorological Data Relay (AMDAR), and AWS surface observations. Three domains are designed for the current study with horizontal grid spacings of 9, 3, and 1 km. The locations of the nested urban domains are shown in Figure 2. NDOWN provides boundary conditions for the 1-km Domain-3 model from its 3-km output, and VDRAS output is assimilated into the 1-km domain via FDDA [41]. Land-use map (Figure 1) is based on 30-m Landsat data for the year 2010, including three urban land types according to gridded urban-fraction values [42]. Parameterization schemes used in this study are listed in Table 1.
\nDomain configuration and location of the study area.
\n | D1 | \nD2 | \nD1 | \n
---|---|---|---|
Models and versions | \nWRFDA v3.5.1 + WRF v3.5.1 | \nWRF v3.5.1 | \n|
Horizontal grid points | \n649 × 400 | \n550 × 424 | \n460 × 403 | \n
∆x (km) | \n9 | \n3 | \n1 | \n
Vertical layers | \n50 | \n||
Cumulus physics | \nKain-Fritsch | \nNone | \nNone | \n
LW radiation | \nRRTM | \n||
SW radiation | \nDudhia | \n||
Microphysics | \nThompson | \n||
PBL physics | \nACM2 | \nBouLac | \n|
Urban physics | \nSLUCM | \nBEP | \nBEP + BEM | \n
Modeling setting and parameterization options.
In order to improve the current forecast model, a cooling tower scheme was incorporated to the BEP + BEM and was coupled with RMAPS. Beijing is taken as the case study to investigate anthropogenic heat impact of dense urban environment. Although the cooling tower scheme has been used to the regional model in previous work, verification and evaluation for improved model are not sufficient especially in vertical stratification. This work used multiple intense observation data to evaluate the improvement effect of the new cooling tower scheme.
\nThe computing method is as follows:
\nBased on the first law of thermodynamics, heat exchange equation between air condition system and the external atmosphere is defined as
\nwhere Q is the total heat transfer from the building calculated by BEM, \n
where \n
The effectiveness, ε, for the cooling tower is defined as:
\nwhere \n
Finally, the outlet air temperature \n
where \n
Due to the effect of complicated urban surface, the structure of the meteorological field in the urban boundary layer is different from other surfaces. Impervious vertical surfaces of buildings induce a drag force that produces a loss of momentum that changes the flow field in near-surface atmospheric boundary layer. The drag coefficient (Cd) is an important component for calculating the magnitude of the momentum flux induced by buildings in urban canopy models. According to previous studies over urban environments and wind-tunnel measurements of, Cd is assumed as a constant (0.4) in default WRF/BEM [40]. However, Cd could vary with building packing densities. An analytical relation proposed by Salamanca and Martilli [27] has been implemented into the BEP + BEM system to estimate the drag coefficient as a function of the building plan-area fraction as follows:
\nThis formulation represents an improvement compared to using a constant drag coefficient, and it is necessary to assess this for a real complex urban underlay.
\nWe evaluated the whole summer (from June 1 to September 30) simulation to evaluate the performance of the RMAPS coupled to the cooling tower model and drag scheme (AC + VD). The forecast results by default RMAPS were used as control run (CTL).
\nThe significant difference between CTL and AC + VD is in latent heat released from the building because AC + VD improved the heat flux released from the building to the environment. Maximum sensible heat flux from air condition in CTL is about 180 W·m−2, while it is reduced to 20 W·m−2 in AC + VD (Figure 3a and b). Meanwhile latent heat flux from air condition is increased by AC + VD during daytime. Thermal exchange between building and its external atmosphere simulation is an inadequate capability in CTL. AC + VD not only increase the latent heat flux released from building but also improve simulation ability of heat exchange.
\nAveraged diurnal pattern of heat flux (W∙m−2). (a) Sensible heat flux in dry day; (b) sensible heat flux in wet day; (c) latent heat flux in dry day; and (d) latent heat flux in wet day.
We evaluate sensible and latent heat flux in the dry day first. Based on the heat flux observation by Beijing tower in 140 m, sensible heat flux is less than 20 W·m−2 in the nighttime, while CTL overestimate sensible heat flux about 50 W·m−2 in the nighttime (Figure 3a). It has largely solved this problem by AC + VD during the dry days. But in the wet days, sensible heat flux is still overestimated in nighttime simulated by both CTL and AC + VD. In the daytime, observed sensible heat flux reaches the maximum (200 W·m−2) at 1400 LST; both CTL and AC + VD overestimate the sensible heat flux in urban area (Figure 3a and b). Sensible heat flux simulated by CTL delays the time to reach the maximum of about 1 hour while an hour earlier by AC + VD. Both CTL and AC + VD overestimate sensible heat flux in urban area, but AC + VD improve the simulation results from 1500 to 2000 LST especially in the dry days. Compared to dry day result, improvement effect for sensible heat flux by ECs is not obvious in wet day. Different from the dry day, the sensible heat flux is rapidly decreasing during the 1100–1800 LST in model results because rainfall often occurs in that duration. And simulated sensible heat flux is more sensitive to precipitation than observed.
\nObserved latent heat flux in dry days has the same features as the sensible heat flux in daytime (Figure 3c). The maximum of observed latent heat flux is about 200 W·m−2, while the latent heat flux is seriously underestimated by CTL in urban area which further leads to error in humidity and temperature. But AC + VD result indicates that model performance for latent heat flux is much improved in both dry day and wet day (Figure 3c and d). The simulated diurnal pattern of latent heat flux by AC + VD is very close to the observation in the dry days although the value is still less than observation and there is phase deviation. There are still errors of latent heat flux simulated by AC + VD in the wet days.
\nBowen ratio is a very important index for the energy balance. Because the latent heat flux is seriously underestimated by CTL, there are big simulation errors for Bowen ratio especially in nighttime, both in dry days and wet days (Figure 4). That problem has been largely solved by AC + VD even though there are still underestimated Bowen ratios especially from 600 to 1600 LST.
\nAveraged diurnal pattern of Bowen ratio. (a) Dry day and (b) wet day.
The heat flux change by AC + VD will further influence temperature and humidity in urban area. So next part we will force on evaluating the model performance of temperature and humidity at 2 m. For the dry day, temperature is obviously overestimated by CTL during the whole day. But performance is largely improved by AC + VD especially in nighttime. Temperature at 2 m is decreased around 3° by AC + VD in nighttime which is very close to the observation (Figure 5a). There are still obvious errors during 1200–1600 LST. For the dry days, improvement for nighttime is still significant (2°) but not as good as wet days when compared to the observation (Figure 5b). Although AC + VD improves the temperature about 1.5°, the maximum simulation deviation still occurred from 1200 to 1600 LST. That is related to overestimating the sensible heat flux. Another error of phase is still in the simulation in dry day.
\nAveraged diurnal pattern of temperature (°C) and absolute humidity (g∙kg−1). (a) Temperature in dry day; (b) temperature in wet day; (c) absolute humidity in dry day; and (d) absolute in wet day.
For the dry day, absolute humidity is underestimated by CTL during the whole day, while it is improved by AC + VD especially in nighttime (Figure 5c). For the wet day, absolute humidity is increasing from 1200 to 1600 because rainfall will more likely occur in this period. While simulated absolute humidity in both CTL and AC + VD lags behind the observation, increasing period is from 1800 to 2300 LST (Figure 5d). Simulated value is improved by AC + VD although the phase difference still remained in both dry day and wet day.
\n294 meteorological stations are used to evaluate model performance of temperature and humidity spatial distribution. Spatial distribution of averaged temperature error shows that CTL overestimate daily mean temperature in most of urban station (Figure 6a), and the errors of most stations reach 1–2°. Errors of temperature are obviously reduced by EC in urban area and errors of about half stations <0.5° (Figure 6b). Both CTL and AC + VD underestimate daily mean absolute humidity at most urban stations. And there is no significant difference or improved effect by AC + VD in mean absolute humidity in urban area.
\nSpatial distribution of averaged temperature (°C) and absolute humidity (g∙kg−1) errors (difference between modeling and observation) at 2 m, the black circles show the second to sixth ring roads. (a) Temperature error in CTL; (b) temperature error in AC + VD; (c) absolute humidity error in CTL; and (d) absolute humidity error in AC + VD.
Maximum temperature (at 3 pm) simulated by CTL is more than 35°C in urban and suburban area in the dry day and 33°C in most of the plains (Figure 7a). While temperature is reduced to about 2° in AC + VD in both urban and suburban areas, and the area in which temperature is more than 36°C is obviously decreased (Figure 7b). There are the same characteristics for wet days (Figure 7c and d).
\nSpatial distribution of averaged temperature (°C) at 2 m in 3 pm. (a) Simulated by CTL in dry day; (b) simulated by AC + VD in dry day; (c) simulated by CTL in wet day; and (d) simulated by AC + VD in wet day.
Spatial distribution of maximum sensible heat flux shows that sensible heat flux simulated by CTL in urban area is more than 350 W·m−2 in the dry day (Figure 8a). The sensible heat flux simulated by AC + VD in most urban areas is about 320 W·m−2 which is a little smaller than CTL (Figure 8b) in the dry day. And the maximum region (more than 350 W·m−2) of sensible heat flux is also reduced by AC + VD. In the wet day, sensible heat flux maximum simulated by AC + VD is smaller about 50–100 W·m−2 than CTL (Figure 8c and d).
\nSpatial distribution of averaged sensible heat flux (W∙m−2) in 3 pm. (a) Simulated by CTL in dry day; (b) simulated by AC + VD in dry day; (c) simulated by CTL in wet day; and (d) simulated by AC + VD in wet day.
Latent heat flux maximum simulated by CTL is less than 50 W·m−2 in downtown area which is obviously underestimated compared to the observation in both dry day and wet day (Figure 9a and c). Simulation of latent heat flux in urban area by AC + VD improves the value to 100 W·m−2 in dry day and 150 W·m−2 in wet day (Figure 9b and d).
\nAs in Figure 8 but for latent heat flux.
Maximum sensible heat flux released by air condition in CTL is more than 100 W·m−2 in urban area, and there is a little difference between dry day and wet day. AC + VD reduce the maximum sensible heat flux released by the building’s air condition in both dry days and wet days (Figure 10a and c). And sensible heat flux in dry day is obviously larger than wet days simulated by AC + VD (Figure 10b and d). That means sensible heat flux released by air condition in AC + VD is affected by outdoor temperature. Model performance of indoor and outdoor exchange is significantly improved by AC + VD.
\nSpatial distribution of sensible heat flux (W∙m−2) released by building air condition in 3 pm. (a) Simulated by CTL in dry day; (b) simulated by AC + VD in dry day; (c) simulated by CTL in wet day; and (d) simulated by AC + VD in wet day.
There is no latent heat flux released by air conditioning used in the potential temperature equation when coupled to the WRF/BEP + BEM in CTL. However, in AC + VD the maximum latent heat flux released is more than 120 W·m−2 in dry days and 80–100 W·m−2 in wet days (Figure 11a and b) over urban core areas.
\nSpatial distribution of latent heat flux (W∙m−2) released by building air condition at 3 pm. (a) Simulated by AC + VD in dry day and (b) simulated by AC + VD in wet day.
The observed daytime change of urban area averaged wind speed appeared as single peak. The wind speed reaches maximum and minimum at 700 LST and 000 LST in summer in Beijing (Figure 12a). The diurnal feature of wind speed is well captured by CTL and AC + VD. However, CTL overestimated wind speed in daytime especially during 500–1000 LST. This problem has been largely solved by AC + VD (Figure 12a). RMSE is also obviously reduced by AC + VD in the whole day (Figure 12b).
\nAveraged diurnal pattern of wind speed and its RMSE (W∙m−2). (a) Averaged of study period and (b) RMSE.
Spatial distributions of wind speed by CTL show that the average wind speed in urban area is 3.5 W∙m−2 (Figure 13a), while the wind speed is reduced in AC + VD about 1.5 W∙m−2 (Figure 13b). The averaged wind speed simulated by AC + VD is about 1.8 W∙m−2. Wind speed in CTL is overestimated in all Beijing areas (Figure 14a). Spatial distributions of wind speed errors also indicated that wind speed error is obviously revised by AC + VD (Figure 14b).
\nSpatial distributions of wind speed and wind fields (W∙m−2). (a) CTL and (b) AC + VD.
Spatial distributions of wind speed errors (m∙s−1) (difference between modeling and observation) at 10 m. (a) CTL and (b) AC + VD.
A cooling tower scheme considering quantitative sensible and latent heat flux released from air condition was coupled to RMAPS. A mechanical drag coefficient formulation was implemented into the RMAPS to improve the representation of the wind speed in complex urban environments. The computing method is based on the heat transfer between temperature and humidity and between condenser and outdoor inlet air. Two simulations use default RMAPS and improved RMAPS to estimate the improvement effect focusing on dry day and wet day, respectively. The cooling tower system in commercial area not only induces the significant increase of the anthropogenic heat partition by 90% of the total heat flux releasing as latent but also further changes the surface heat flux feature. When cooling tower is introduced, averaged surface latent heat flux in urban area is increased to about 60 W·m−2 with the peak of 150 W·m−2 in dry day and 40 W·m−2 with the peak of 150 W·m−2 in wet day. Further new cooling tower scheme improves the model performance of temperature and humidity. Maximum and minimum temperature error improves 2–3° especially in dry day. The drag coefficient formulation induced the simulated wind speed to about 2.5 m∙s−1 that improve the wind speed error of about 2–3 m∙s−1 in urban area.
\nThis work was supported by National Natural Science Foundation of China (grant 41705090).
\nInformal economies have long been the linchpin of food security for both the rural and urban poor in developing countries [1]. The most visible activities in the informal sector are food production (urban, peri-urban and rural), processing, catering and transport and retail sale of fresh or prepared products (e.g. street food) with women being responsible for small catering operations and street food sales [2]. Women are traditionally skilled in these activities and already have the necessary cooking utensils at home, while the food they prepare also enables them to feed their families [3].
Most of the eggs, fish, meat and milk sold to the poor in urban Africa are sourced from informal markets [4]. For example, in countries like Kenya, Mali and Uganda, 80–90% of raw milk is purchased from vendors or small-scale retailers in informal markets [4]. Informal markets in this regard contribute to food security and do so in various ways. First, informal markets tend to be located closer to low-income housing settlements and thus serve as the main source of food for many of the urban poor [1]. Second, informal vendors can sell in smaller quantities, at lower prices and on credit, thus making food affordable to marginalised consumers [2, 5]. Third, the many points of sale near schools and offices, bus stops and stations facilitate access to food, thus saving consumers’ time and transport costs [2]. Fourth, the ability of poorer households to produce their own food products especially in situations of economic uncertainty can be crucial for food security [2].
Despite this contribution to food security, the safety of food sold in the informal sector has been raised as a matter of concern. Traders in informal markets operate in settings without electricity, clean potable water, waste disposal and sanitation facilities [4, 5]. These settings are sometimes dusty and muddy and/or may sometimes be flooded due to poor roads [6]. Such an environment exposes food to contamination, thus increasing the risk of foodborne illnesses [6].
The implementation of food safety in the informal sector has traditionally focused on enforcement mechanisms to remove “unsafe” food from the market [3, 7]. This reactive approach which is also a response to other non-compliances of the sector such as tax evasion, trespassing on private land and traffic congestion mainly targets street vendors and typically involves issuing of fines, making of arrests, confiscation of merchandise and demolishing of market stalls [7].
The sole focus of these campaigns on street vendors in the drive towards implementation of food safety in the informal sector somehow fails to hold producers (including urban and peri-urban agriculturalists), transporters, processors (including home-based caterers), managers, small restaurant owners, members of cooperatives etc. accountable as these stakeholders also have a role to play in ensuring the production, processing, packaging and distribution of safe food along the food chain. As a result, such campaigns have been rendered ineffective (see, e.g. [1, 4]).
This chapter makes reference to case studies conducted by the International Livestock Research Institute’s Safe Food Fair Food project to facilitate the implementation of food safety in Sub-Saharan Africa. The purpose is to illustrate the relevance and the applicability of a food chain approach in the implementation of food safety in the informal sector. The chapter is organised as follows. Section 2 presents a theoretical framework with the first sub-section outlining the food chain approach in the implementation of food safety followed by foodborne illnesses and their impact on public health. The methodology section follows after which we present the results. The chapter then draws conclusions and outlines implications for stakeholders concerned and some recommendations for further research.
For food to be regarded as safe, there should be assurance that it will not cause harm to the consumer when prepared and/or eaten according to its intended use [8].
The food chain approach acknowledges that the responsibility to provide safe, healthy and nutritious food lies with all the actors taking part in the production, processing, trade and consumption of food along the entire food chain from primary production to final consumption [3]. Chain actors include food producers, fishermen, abattoir operators, food processors and distributors including wholesalers and retailers. This responsibility extends to the end-consumer who must be educated to ensure that food is properly stored and hygienically prepared and food shelf lives are respected [3]. Government departments with a mandate to render public health services are also implicated in this regard [3].
The food chain approach uses a scientific, rational and systematic approach known as the Hazard Analysis and Critical Control Points (HACCP) to identify, assess and control hazards during production, processing, manufacturing and preparation of food. For HACCP to be effective, a strong foundation of safety-related prerequisites is essential.
Prerequisite programmes include, among others, the implementation of good manufacturing practices, good agricultural practices, good hygiene practices, sanitation, standard operating procedures, employee hygiene and training, product labelling and coding, facilities design, equipment calibration and maintenance. Food safety along the value chain thus promotes the adoption of conditions and practices that preserve the quality of food in order to prevent contamination and the resulting foodborne illnesses [8].
Foodborne illnesses are diseases caused by microbiological, chemical and physical agents that enter the body through the ingestion of contaminated food and/or water used for its production, processing and preparation [2, 3, 9]. In humans, foodborne illnesses are linked to more than 250 different types of viruses, bacteria, parasites, toxins, metals and prions [10] and are usually either infectious or toxic in nature. They are at best unpleasant and at worst fatal.
The infections range from mild gastro-enteritis to life-threatening neurological, hepatic and renal syndromes caused by either toxin from the disease-causing microbes or by the human body’s reaction to the microbe itself [11]. Although viruses are more responsible for more than 50% of all foodborne illnesses, generally hospitalizations and deaths associated with foodborne infections are due to bacterial agents [11]. Of the many thousands different bacterial species, more than 90% of food poisoning illnesses are caused by species of Staphylococcus, Salmonella, Clostridium, Campylobacter, Listeria, Vibrio, Bacillus and Enteropathogenic coli [12]. Around 2 million people die every year from diarrhoeal diseases largely due to contaminated food and water [2, 3].
The contamination of food may occur at any stage in the process from production to consumption (farm to fork or stable to table) and can also result from environmental contamination including pollution of water, soil or feeds [2, 3]. Improving the safety and quality of food at all stages of the food chain is therefore important in reducing the risks associated with the consumption of contaminated food and the resulting foodborne illnesses.
For over a decade, the International Livestock Research Centre (ILRI) and partners have been conducting research on food safety in informal markets in Sub-Saharan Africa. The objective is to support intensification of livestock production by building capacity for better management of safety of animal source food products. The ultimate goal is to maximise market access for the poor dependent on livestock and livestock products while minimising foodborne illnesses for poor consumers [13]. While the focus of ILRI’s Safe Food Fair Food project was livestock and livestock products, the majority of the cases studied milk in informal markets. This chapter therefore focuses on informal milk marketing in Sub-Saharan Africa.
Milk produced in the informal sector often leaves the farm gate through three channels, namely, collectors who sell the milk to the informal market, sale to the dairy cooperative and/or sale within the community [14]. Collection of milk is carried out at milk collection centres by dairy cooperatives or private milk collectors [14, 15]. Milk producers and other traders transport their milk on foot, by horse and by donkey and/or make use of a private milk transporter to deliver the milk to the milk collection centres and to the processing plant [14].
Alternatively, milk producers take their raw milk to the milk collection points. From the milk collection points, the milk is transported to a dairy plant or to the milk collection centres where it is bulked, cooled and transported to the dairy plant. Private milk collectors and dairy cooperatives operate milk collection points and milk collection centres [14]. The dairy cooperatives are mainly involved in bulk collecting of raw milk from the members in order to process the collected milk into different dairy products [14]. Informal milk marketing thus involves direct supply of fresh raw milk by small-holder dairy producers to the neighbouring final consumers and to the traders or individuals in the nearby cities.
This chapter makes reference to case studies conducted by the International Livestock Research Institute under the Safe Food Fair Food project in Sub-Saharan Africa. Secondary data sources of information are also consulted to complement the data.
This chapter applies guidelines on good agricultural practices, good manufacturing practices and good hygiene practices to offer justification as to why it is important to adopt a food chain approach in the implementation of food safety initiatives in the informal sector. The chapter does so by making reference to the case studies on milk production in Sub-Saharan Africa by the International Livestock Research Institute under the Safe Food Fair Food project.
Like any other food business, milk producers must aim to ensure that the safety and quality of the raw milk produced will satisfy the highest expectations of the food industry and consumers [16].
Good farming practices underpin the marketing of safe, quality-assured milk-based products [16]. This means that on-farm practices should ensure that milk is produced by healthy animals under acceptable conditions for the animals [16]. The focus of on-farm practices should be on preventing a problem (including animal diseases) rather than solving it after it has occurred [16]. However, according to Desissa et al. [17], 92% of dairy farmers indicated that their cows had recently experienced inflammation of the udder. Kilango et al. [18] also reported that almost 90% of the farmers had encountered udder infections in their animals. Only 21% of the farmers were reported to have used post-milking teat disinfectant which is known to be effective in preventing udder inflammation [18].
Milk producers are required to manage the use of all chemicals, e.g. detergents, disinfectants, antiparasitics, antibiotics, herbicides, pesticides and fungicides, to prevent unacceptable chemical residues occurring in the milk and unsuitable chemicals adversely affecting animal health and productivity and are expected to be aware of all chemicals that may leave residues in milk [16]. Milk producers are also required to observe withholding periods, i.e. the minimum times when milk should not be sold for human consumption after application of chemicals [16]. Interestingly, most of the farmers knew about the proper use of antibiotics to treat dairy cows and the importance of adhering to the withdrawal period for milk from cows undergoing treatment [18]. Yet in a study done by Kouame-Sina et al. [19], almost a quarter (24.7%) of the milk samples contained antibiotics.
Milking is the most important single activity in a dairy farm [16]. FAO [16] recommends that before a cow is milked, the foremilk should be extracted and checked for abnormalities. It is also expected of farmers to follow appropriate procedures to separate milk from sick animals and animals under treatment. For example, sick animals could be milked last into a separate container and/or separate facilities may be provided if possible [16]. However, Kouame-Sina et al. [19] reported that milk from cows with udder inflammation (mastitis) was still sold.
Yobouet et al. [20] and Kone et al. [21] also observed that while milking, milkers often dipped their fingers into the milk to use it as a lubricant to help them milk faster. The farmers considered it necessary to grease the teats with milk. However, this practice presented a risk of milk contamination [21].
FAO [16] recommends that the disposal of diseased and dead animals should be done in a way that minimizes the risk of disease spread. According to Sow et al. [22], about 12% of farmers reported their cows had aborted once, and approximately 2% reported that their cows aborted twice, while 27% reported their female goats had aborted once, 4% twice and 1% thrice. Almost 75% of these respondents reported they handled the aborted material of their goats; for instance, they would handle the foetus with bare hands and later burn it [22]. While the latter practice reduces the risk of transmission of brucellosis, touching the aborted material with bare hands makes transmission very likely [22].
Good milk is considered to be cow milk that is not mixed with that from other species or milk powder [21] or any additives or adulterants. However, it was found out that producers sometimes put additives in milk to increase fat content. They also add water in milk to increase volumes. This practice, which is commonly known as adulteration, is done in an attempt to get fair and remunerative prices for the milk. In the dairy industry in West and East Africa, it seemed to be common practice to add water to milk in order to sell large quantities. Yobouet et al. [20] also observed that adulteration of milk by adding water was relatively widespread among retailers, with 47.1% of them reportedly adding water to the milk before selling it.
Similarly, in a case study by Kouame-Sina et al. [19], 50% of the milk on sale was adulterated with water. Adulteration of milk is not only illegal but also presents a health risk to consumers if the added water is contaminated [20]. With adulteration, consumers get less of what they paid for [21]. This practice is also unethical. According to Desissa et al. [17], collection centres checked the milk for adulteration with water and contamination with hair, hay or faeces. Milk that does not pass the quality test is rejected and is mostly processed by the women into féné (partially fermented milk), which may present a health risk [21]. More than 25% of the vendors did not use any form of quality control checks before buying the milk [18] which meant that they could not establish whether the milk they bought was contaminated or not.
Because of its high perishability, holding milk for long periods without cooling it can lead to rapid growth and multiplication of the milk bacteria [18]. To sustain quality, milk is generally cooled after harvesting.
To cool the milk, some producers predominantly used package icing. The ice blocks are purchased from vendors who make them by filling polythene pouches with tap water and packing the pouches in a freezer [23]. While water and ice cooling methods provide fast initial cooling, they also feature the highest risk of contamination.
In the cases studied by Spengler et al. [24], none of the farmers in the study sites cooled the raw milk. The traders and retailers also reported that the milk they received from the farms was not cooled [20].
Pooling of milk was also reported to increase the risk of contamination. Yobouet et al. [20] reported that upon receipt of milk from farmers, traders and retailers bulked the milk in a single storage tank. This is risky because, if the milk from one supplier is contaminated, then there is a higher chance that the entire batch in the tank will be spoiled. According to Desissa et al. [17], only milk from cooperative members was purchased and pooled into a 50 litre container.
With regard to packaging, all of the vendors used plastic buckets for handling the milk, a practice that is not recommended as plastic containers are known to be vulnerable to bacterial contamination due to difficulty to sterilise [18]. This was also observed by [23].
Workers can unintentionally contaminate milk, water supplies and equipment, thus transmitting diseases to other workers or consumers. Proper hand-washing, correct washing procedures including applying soap on wet hands, scrubbing of whole hand during a minimum of 20 s, rinsing with water, drying with paper towel (disposable) and turning off the knob with towel are recommended as part of enhancing food safety [8].
It was interesting to observe that at least some of the recommended hygiene practices were followed. However, some of the procedures applied were not completely correct which could further contaminate the milk. For example, milkers cleaned the udders of the cows either with dry hands or with water that was not of potable quality [24]. Yobouet et al. [20] and Kouame-Sina et al. [19] both observed that prior to milking, most milkers washed neither their hands nor the udders of the cows, while in other cases, the majority of the farmers washed their hands before milking [25]. Kabui et al. [25] also observed that the majority of those who washed their hands used water and soap [25], while in a case study by Kilango et al. [18], only 17% of milkers washed their hands with water and soap. Farmers only cleaned their hands with cold water from a contaminated water source [24].
Almost all milkers washed the cow udders before milking. However, most of the milkers did not dry their hands or the udders of the cows, thus increasing the risk of contamination from dirty water running into the milk [18].
Using one cloth for each cow to dry the udder is recommended as it prevents the transfer of bacteria and dirt from one animal to another. Kilango et al. [18] observed fewer milkers used one cloth per cow to dry the udder, and according to these authors, using a shared cloth for several cows may be worse than using no cloth at all [18].
Proper personal hygiene is also important to enhance food safety in informal milk markets. Regular bathing or showering, washing of hands and keeping nails clean and short and wearing of clean clothes and hair nets are thus recommended. According to Kilango et al. [18], only 59% of the staff working at milk kiosks wore clothes that were visibly clean.
Proper cleaning of milk utensils is also instrumental in the implementation of food safety in informal milk markets. About 82% of the staff used soap for washing utensils [18]. Those without basins but with running hot water cleaned the milk handling equipment in the same bowl used for washing hands [18]. This practice is not good as it may increase the risk of contamination.
While vendors are at the forefront of milk sales in informal markets, the informal market environment seemed not to be the only source where food can get contaminated as is always perceived. Results show that some milk got contaminated during milking, cooling, pooling, processing, packaging, handling and cleaning of milking utensils which means that other stakeholders who handled the product were also responsible for milk contamination but were not held accountable for supplying a product that is “unsafe” as perceived by the authorities. Ensuring food safety along the chain therefore becomes the responsibility of everyone who has a role to play. The fact that a high number of incidents of foodborne illnesses is recorded in informal economies is also enough evidence to warrant the implementation of food safety practices in informal economies but a correct approach and strategies need to be adopted. Compliance of relevant actors with food safety principles should therefore be encouraged.
These finding have implications for dairy farmers/milk producers, milkers, retailers/milk kiosks operators, milk cooperative members, processors, traders/vendors, households and consumers of milk. These stakeholders all need to be aware of food contamination and the risks and consequences of foodborne illnesses. The findings also imply that stakeholders need to be proactive in their drive to ensure food safety along the food chain which means that they need to adhere to good agricultural practices, good hygiene practices as well as good manufacturing practices. Training of households on basic hygiene is also crucial. This could also help minimise the incidents of foodborne illness that threaten the lives of consumers.
To take the food safety agenda forward, future research should focus on developing a focused strategy for the implementation of food safety in the informal sector. This should take into consideration the context and the importance of food security in rural livelihoods.
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