Summary of recent studies published on the extraction of plant materials optimized by RSM.
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The parameters that affect the process are called dependent variables, while the responses are called dependent variables [3].
\nFor example, the hardness of a meat is affected by cooking time X1 and cooking temperature X2. The meat hardness can be changed under any combination of treatment X1 and X2. Therefore, time and temperature can vary continuously. If treatments are from a continuous range of values, response surface methodology is useful for developing, improving, and optimizing the response variable. In this case, the hardness of meat Y is the response variable, and it is a function of time and temperature of cooking. It can be expressed as the dependent variable y is a function of X1 and X2.
\nwhere Y is the response (dependent variable), X1 and X2 are independent variables and e is the experimental error.
\nResponse surface is a method based on surface placement. Therefore, the main goals of a RSM study are to understand the topography of the response surface including the local maximum, local, minimum and ridge lines and find the region where the most appropriate response occurs [4].
\nThe RSM investigates an appropriate approximation relationship between input and output variables and identify the optimal operating conditions for a system under study or a region of the factor field that satisfies the operating requirements [5, 6]. Box-Behnken designs (BBD) and central composite design (CCD) are two main experimental designs used in response surface methodology [3]. Central composite rotatable design (CCRD) and face central composite design (FCCD) has also been applied to optimization studies in recent years [7, 8, 9].
\nThe experimental data are evaluated to fit a statistical model (Linear, Quadratic, Cubic or 2FI (two factor interaction)). The coefficients of the model are represented by constant term, A, B and C (linear coefficients for independent variables), AB, AC and BC (interactive term coefficient), A2, B2 and C2 (quadratic term coefficient). Correlation coefficient (R2), adjusted determination coefficient (Adj-R2) and adequate precision are used to check the model adequacies; the model is adequate when its P value < 0.05, lack of fit P value > 0.05, R2 > 0.9 and Adeq Precision >4. Differences between means can be tested for statistical significance using analysis of variance (ANOVA) [10].
\nThe design of experiments (DoE) is the most important aspect of RSM. The DoE aims the selection of most suitable points where the response should be well examined. The mathematical model of the process is mostly related to design of experiments. Thus, the selection of experiment design has a great effect in determining the correctness of the response surface construction. The advantages offered by the RSM can be summarized as determining the interaction between the independent variables, modeling the system mathematically, and saving time and cost by reducing the number of trials [11]. However, the most important disadvantage of the response surface method is that the experimental data are fitted to a polynomial model at the second level. It is not correct to say that all systems with curvature are compatible with a second-order polynomial model. In addition, experimental verification of the estimated values in the model should be done absolutely [3].
\nIn early stage of DoE, screening experiments are performed. If there are many variables have little or more effect on the response, the variables which have large effects on response are identified. Therefore the aim is to determine the design variables that have large effects for further investigation [12].
\nUsing Response Surface Method in the extraction studies has been of interest to many researchers in recent years [10, 13, 14]. The steps that must be followed in order to apply this method correctly are shown in Figure 1.
\nSteps for response surface methodology.
Recent optimization studies using the response surface method in extraction from plant materials are summarized in Table 1. Independent and dependent variable numbers and the optimization designs are also demonstrated in the same table.
\nExtraction | \nExtraction method | \nProcess parameters | \nDesign method | \nDependent variables | \nModel | \nRef | \n|
---|---|---|---|---|---|---|---|
Olive leaf | \nUltrasound assisted extraction | \nSolvent concentration, the ratio of solid to solvent, extraction time | \nBBD | \nExtract yield, total polyphenol content, antioxidant activity | \nQuadratic polynomial | \n[27] | \n|
Olive waste | \nNon-conventional aqueous extraction method | \nNaOH, temperature, time, mass of the waste | \nBBD | \nTotal phenolic content, relative color strength | \nQuadratic polynomial | \n[29] | \n|
Olive leaf | \nSolvent-free microwave-assisted extraction | \nAmount of sample, irradiation power, the extraction time. | \nFCCD | \nOleuropein yield and total phenolic content | \nQuadratic polynomial | \n[9] | \n|
Olive oil | \nUltrasound assisted extraction | \nUltrasound time, ultrasound temperature, malaxation time | \nBBD | \nOil yield, acidity | \nQuadratic polynomial | \n[10] | \n|
Olive oil | \nHigh power ultrasound assisted extraction | \nOlive paste flow, ultrasound intensity, fruit temperature before crushing, olive moisture, olive fat content | \nBBD | \nOlive paste temperature | \n2FI | \n[17] | \n|
Olive oil | \nConventional extraction | \nMalaxation time and temperature | \nCCD | \nAcidity, peroxide value, K232, K270, Total phenolic content | \nQuadratic polynomial | \n[32] | \n|
Black Carrot | \nUltrasound assisted extraction | \nUltrasound energy density, temperature | \nCCD | \nAnthocyanin compounds | \nQuadratic polynomial | \n[13] | \n|
Curry leaf | \nUltrasound assisted extraction | \nTemperature, ultrasonic power, methanol concentration | \nCCD | \nCatechin yield, myricetin yield, quercetin yield, antioxidant activity | \nQuadratic polynomial | \n[20] | \n|
Rapeseed meal | \nUltrasound assisted solvent extraction | \nTemperature, liquid to material ratio, duration and ultrasonic power | \nBBD | \nCarotenoid yield | \nSecond-order (Quadratic) polynomial | \n[31] | \n|
Gac fruit peel | \nSolvent extraction | \nExtraction time, extraction temperature, solvent ratio | \nBBD | \nTotal carotenoid, Antioxidant capacity | \nQuadratic polynomial | \n[30] | \n|
Coffee silverskin | \nUltrasound assisted extraction/Microwave assisted extraction | \nExtraction time, extraction temperature | \nCCD | \nTotal phenolic content, radical scavenging capacity, total caffeoylquinic acids, caffeine content | \nQuadratic polynomial | \n[21] | \n|
Brown seaweed | \nUltrasound assisted extraction | \nExtraction time, acid concentration, ultrasound amplitude | \nBBD | \nTotal phenolic, fucose, uronic acids | \nSecond-order (Quadratic) polynomial | \n[35] | \n|
Hazelnut skin | \nUltrasound assisted extraction | \nExtraction time, temperature, ultrasound amplitude | \nCCD, BBD | \nCrude polysaccharide yield, consumed energy | \nQuadratic polynomial | \n[25] | \n|
Trapa quadrispinosa stems | \nUltrasound assisted extraction | \nUltrasonic time, liquid to material ratio, ultrasonic temperature | \nBBD | \nPolysaccharide yield, Ferric-Reducing Antioxidant Capacity (FRAC) | \nQuadratic polynomial | \n[26] | \n|
Sphallerocarpus gracilis roots | \nHot water extraction, Ultrasound assisted extraction | \nExtraction temperature, Extraction time, Liquid–solid ratio, Ultrasound power | \nBBD | \nS. gracilis yield | \nQuadratic polynomial | \n[33] | \n|
Papaya seed oil | \nUltrasound assisted extraction | \nTime, temperature, ultrasound power, solvent to sample ratio | \nSCCD | \nYield, antioxidant activity, p-anisidine value, peroxide value, totox value | \nQuadratic polynomial | \n[18] | \n|
Pomegranate seed oil | \nUltrasound assisted extraction | \nUltrasonic power, extraction temperature, extraction time, the ratio of solvent volume and seed weight | \nBBD | \nOil yield | \nQuadratic polynomial | \n[19] | \n
Summary of recent studies published on the extraction of plant materials optimized by RSM.
Extraction yield is one of the main properties determining efficiency of olive oil extraction. This parameter indirectly takes into account the oil content held in vegetable water and pomace [15, 16].
\nExtraction yield is defined as the percentage of the extracted olive oil from the total weight of fruit (g). The extraction yield is calculated using the formula below [10]:
\nAydar et al. used olive fruits (Olea europaea L.) from Edremit cultivar grown in Mut area were harvested in the 2015 crop season with a maturity index of 3.35 to obtain ideal conditions for an ultrasound assisted olive oil extraction. It was aimed an extraction for extra virgin olive oils in low acidity and high yield using the Box-Behnken design to optimize extraction parameters including ultrasound time, ultrasound temperature and malaxation time [10].
\nIn terms of yield, the independent variable (X2), the quadratic term (X22) and the interactive terms (X1X2, X2X3) were all significant (P < 0.05). The quadratic regression model for AV was as follows:
\nThe most significant effect on the extraction yield (P < 0.05) was the malaxation temperature among all ultrasound extraction variables. Conversely, ultrasound time showed no effect (P > 0.05) on the yield [10].
\nThe response surface methodology has been applied to determine the optimization of olive paste heating and how it is affected by the independent process variables including olive paste flow (Q), high power ultrasound (HPU) intensity (W), olive temperature (OT), olive moisture (OM) and olive fat content (OF) by Bejaoui et al. [17]. They obtained a 2FI (two factor interaction) model for olive paste temperature according to the analysis of variance which showed that the regression model was significant for a P-value <0.0001. The most significant terms of the model were Q, W and the interaction terms Q*W and W*OF based on P-values less than 0.0001 [17].
\nSecond-order equations for oleuropein yield was shown in Eq. (4) [9]
\nWhere X1 is the amount of sample, X2 is the Microwave (MW) irradiation power, and X3 is the extraction time. The researchers found that the second power of microwave intensity was the most significant parameter, followed by the amount of sample, quadratic time, and power for oleuropein yield [9].
\nResponse surface method has been used frequently in recent years to optimize different oil extractions other than olive oil including papaya seed oil and pomegranate seed oil [18, 19].
\nTo optimize the ultrasound-assisted extraction conditions followed by ultrahigh performance liquid chromatography (UHPLC) to achieve high catechin, myricetin, and quercetin contents, and high antioxidant and anticancer activities in the curry leaf extracts, RSM was applied by Ghasemzadeh et al. [20]. They used the central composite experimental design (3-level, 3-factorial) to determine the optimum extraction parameters affecting the extraction yields of catechin (Y1), myricetin (Y2), quercetin (Y3), and antioxidant activity (Y4) of curry leaf extracts [20].
\nThe extraction efficiency of UAE and MAE methods was compared to a conventional solvent extraction by Guglielmetti et al. [21]. Authors used RSM with a CCD to investigate ultrasound assisted extraction (UAE) and microwave assisted extraction (MAE) of caffeoylquinic acids and caffeine from coffee silverskin (CS) at two particle size. They found that the highest caffeine content (14.24 g kg−1 dw) with a significant reduction of extraction time was obtained by UAE [21].
\nSince different extraction methods have important impacts on the polysaccharide bioactivity, yield and structure, to find the best extraction method to obtain high yield of polysaccharide is crucial. Recently several researchers used RSM for optimization of polysaccharide extraction from different plant materials [22, 23, 24, 25, 26]. To investigate the best response surface design for optimization of polysaccharide yield (CPS) from hazelnut skin, CCD and BBD designs were studied by Yılmaz and Tavman [25]. Optimum conditions for a maximum yield of polysaccharide extraction from Trapa quadrispinosa stems recently determined by Raza et al. 41 min, 31.5 mL/g and 58°C were the optimum conditions for extraction time, ratio of water to material, and extraction temperature, respectively [26].
\nIn recent years, there has been a growing interest in finding new natural sources of food antioxidants. As a main fruit crop, olive is also valued due to its phenolic- containing leaves. Optimization of ultrasound-assisted extraction of olive leaf has been studied by extraction parameters including solid/solvent ratio, time and ethanol concentration by Şahin and Şamli [27]. In order to obtain the maximum extraction performance for an ultrasound assisted extraction, 500 mg olive leaf to 10 mL solvent ratio, 60 min of extraction time and 50% ethanol composition were found to be as optimal operating conditions [27].
\nShirzad et al. also studied on optimization of olive leave extraction in order to shorten the time of extraction and decrease the consumption of energy. The conditions for obtaining maximum yield of polyphenols, total flavonoids and antioxidants were optimized using RSM The effects of ultrasonic temperature (35–65°C), ultrasonic time (5–15 min), and ethanol to water ratio (Et:W) (25–75%) were evaluated. The highest extraction yield was found to be 51% of ethanol to water ratio at 65°C for 15 min [28].
\nElksibi et al. used RSM to investigate the optimization of natural colorant non-conventional extraction technique from olive waste. They studied the combined effects of extraction conditions on total phenolic content (TPC) and relative color strength (K/S) using a three-level three-factor Box-Behnken design [29].
\nSecond-order equation for total phenolic content from olive leaf obtained by RSM was shown in Eq. (5) by Şahin et al. [9]:
\nwhere X1 is the amount of sample, X2 is the MW irradiation power, and X3 is the extraction time [9].
\nAgcam et al. [13] used response surface methodology to optimize ultrasound assisted anthocyanin compounds extraction from black carrot. The independent variables were temperature and ultrasound energy density which is calculated with following Eq. (6):
\nThe optimization of five different anthocyanin compounds from black carrot was conducted using CCD design with a 16 factorial experiments, 5 replicates of the central point. They obtained quadratic polynomial equations for each anthocyanin compound which were cyanidin-3-xylosyl-glucosyl-galactoside (C3XGG),cyanidin-3-xylosyl-galactoside (C3XG), monoacylated anthocyanins cyaniding-3-xylosyl-glucosyl-galactosidesinapic acid (C3XGGS), cyanidin-3-xylosyl-glucosylgalactoside-ferulic acid (C3XGGF), and cyanidin-3-xylosyl-glucosyl-galactoside-coumaric acid (C3XGGC) [13].
\nGhasemzadeh et al. [20] found that ANOVA for predicted model of antioxidant activity was significant (F-value 17.21, P < 0.0001) with a good coefficient of determination (R2 = 0.98). They also observed that extraction variables showed significant (P < 0.01) quadratic and linear effects on the antioxidant activity and the predicted model obtained for DPPH (Y4) was as follows:
\nWhere X1 is the temperature, X2 is the methanol concentration, and X3 is the ultrasonic power.
\nUsing RSM the extraction conditions including extraction time, temperature and solvent–solid ratio were optimized for maximizing extraction yields of carotenoids and antioxidant capacity from Gac fruit peel by Chuyen et al. [30]. In that study most effective solvent was ethyl acetate and optimal extraction conditions (time, temperature and solvent-solid ratio) were 150 min, 40.7°C and 80 mL g−1, respectively [30].
\nBox-Behnken design (BBD) with a total number of 29 experiments were conducted for four factors (temperature, liquid to material ratio, duration and ultrasonic power) and at three levels to obtain high yield of carotenoid from rapeseed meal. Optimal ultrasound assisted extraction conditions were as follows: temperature 49.6°C, liquid to material ratio 41.4 mL/g, duration 48.5 min, ultrasonic power 252.9 W [31].
\nGuglielmetti et al. observed a positive correlation between an increase of temperature and total phenolic content (TPC) for conventional solvent extraction and UAE; a negative effect on TPC when using MAE above 50°C. They found that temperature was the most effective process variable on extraction processes [21].
\nEspínola et al. used RSM to investigate the optimum extraction condition for virgin olive oil extraction from olives at three different maturation index (MI). In olives at lowest maturity index, temperature had a positive effect on polyphenol content at low malaxation temperatures, however no significant effect was determined at higher temperatures. On the contrary, malaxation time had a slight influence at lower temperatures. In higher MI olives, variations of polyphenol content were not significantly different [32].
\nIn the response surface method, the model that best represents how dependent variables are affected by independent variables is determined theoretically. However, experiments should be carried out to verify the reliability of the theoretically determined models under optimum conditions. Chi-Square test and t-tests are most commonly used to determine the difference between experimental and predicted values. Another method to evaluate the validation of model is to calculate experimental error between theoretical and experimental values.
\nThe experimental and predicted values were 8.31 and 8.42% for the acidity and the yield were 0.31 g oleic acid/ 100 g olive oil and 0.28 g oleic acid/ 100 g olive oil for predicted and experimental values, respectively. These results were in good agreement with the predicted values under the optimum working condition. Therefore, the acidity value of olive oil and yield for any combination of ultrasound time, ultrasound temperature and malaxation time could be accurately predictor by the regression models obtained by RSM [10]. In the 2005–2006 season, the estimated extraction yield, acidity and peroxide index of the 3.2 MI olive samples showed that the experimental data were consistent with the model for all three dependent variables [32].
\nElksibi et al. found that experimental value of 22.54 and 1120 mg/L for the color strength parameter (K/S) and the total phenolic content, respectively. While the predicted values were 23.22 and 1134 mg/L for the color strength parameter (K/S) and the total phenolic content, respectively. They determined the results obtained at the optimal combination was in agreement with the theoretical result. Therefore, the model obtained in this research was confirmed [29].
\nThe experimental extraction yield in the hot water extraction process was 3.79 ± 0.13% and the yield in the ultrasound extraction process was 6.04 ± 0.21% under the optimum conditions, which were in good agreement with the predicted values. These results demonstrated that the extraction models were reliable and accurate [33].
\n15 min, 45°C and 50% amplitude was selected as an optimal level of parameters to validate the result of desirability functions. 1.69% CPS yield and 73.00 kJ energy consumption were found and the predicted values obtained by CCD and BBD were similar to the experimental values and the points of all predicted and experimental response values were correlating. Thus the model developed was significant and reliable. Studentized test results were in agreement with experimental runs which showed that all the data points were kept within the limits [34].
\nValidation of the regression equation and statistical model was conducted at 49.6°C, 41.4 mL/g, 48.5 min and 240 W which were temperature, liquid to material ratio, extraction time and power of ultrasound, respectively. With these optimized conditions, the predicted response for carotenoid yield was approximately 0.1570 mg/g, and the experimental value was found as 0.1577 ± 0.0014 mg/g. These results confirmed that experimental values are in agreement with the predicted values, thus the model was validated [31].
\nResponse surface methodology with a wide range of applications in food science and technology has been successfully used for many years. Optimization of the extraction of plant materials known to be useful for health has attracted many researchers in recent years. This section summarizes the recent researches that optimize extraction conditions necessary to obtain higher quality and yield than plant materials using RSM. One of the most important points in the implementation of this method is that the predicted values in the model should be verified experimentally. RSM has many advantages when compared to classical methods. It needs fewer experiments to study the effects of all the factors and the optimum combination of all the variables can be revealed. The interaction (the behavior of one factor may be dependent on the level of another factor) between factors can be determined. It also requires less time and effort. With all of these advantages, it will be used not only in food science but also in other areas in future.
\nRenewable energy remains the most dominant energy source in Africa with wood sources accounting for a large share of biomass energy. Although wood energy accounts for only 10% of global primary energy, about 2.8 million people depend on wood fuel for cooking and heating [1, 2]. The extraction and use of wood for energy is prominent in developing countries with more than 70% of households in Sub-Saharan Africa depending on wood energy. Access to modern energy remains a major problem in developing countries; however, poorer countries suffer more from energy access problems [3]. Poor access to modern energy rates in less developed countries (LDCs) and Sub-Saharan (SSA) countries remain high at 91 and 83%, respectively. In SSA, the access to electricity and modern energy remains a major constraint with 560 and 625 peopled deprived, respectively. Poor access to modern energy equally varies between urban and rural areas in Africa; in SSA, 66% of the population use solid fuels for heating and cooking, 13% use charcoal while kerosene, electricity and LPG follow with 7, 6, and 5%, respectively [4].
The global use of wood fuel for cooking and heating has devastating negative health effects with 2 million deaths annually from pneumonia, cancer and chronic lung diseases due to exposure to pollution from biomass combustion. Women and children are most affected by these diseases with about 44% of these deaths being children and 60% of adult death being women [3]. More than 50% of deaths from pneumonia, cancer and chronic lung diseases in LDCs and SSA is due to combustion of solid fuels, while only 38% for developing countries in general [3]. Household air pollution (HAP) is a major driver of global health emergencies with about 4.3 million premature deaths; non-communicable diseases (NCDs) account for 3.8 million deaths (WHO, 2016). HAP accounts for more than 33% deaths related to chronic obstructive pulmonary in both low- and middle-income countries, 17% of deaths related to cancer, 15% of ischaemic heart disease and 25% stroke-related deaths (WHO, 2016). This chapter seeks to review the different diseases caused by incomplete combustion of biomass for energy and how bioenergy from tree commodities can be a sustainable remedy.
Several scientific publications have reported significant health effects of wood fuel combustion for cooking especially through open fire in rural areas [5, 6]. Childhood respiratory infections such as pneumonia and otitis media have been highly associated with fuel wood combustion [5]. Among women, there is a high association between fuelwood combustion and high risk of chronic bronchitis and chronic obstructive pulmonary disease, especially asthma and cataract. Indoor combustion of fuelwood has been called the ‘kitchen killer’ because about 1.6 million deaths have been registered as a result, accounting for 2.7% of global disease burden (WHO, 2007).
The combustion process generates smoke; this smoke contains a complex mixture of numerous particles and substances composed of varied organic and inorganic compounds [7, 8]. These compounds are toxic and dangerous to the health system of human beings; they contain carbon monoxide (CO), nitrogen and sulphur oxide (NO2, CO2), aldehydes, particulate matter PM (PM10), volatile organic compounds, chlorinated dioxins, free radicals and polycyclic aromatic hydrocarbons [8]. The health effects on children less than 5 years and women are not homogenous. Respiratory infections such as pneumonia are common in young children less than 5 years, while chronic obstructive pulmonary disease (CORP) and lung cancer are common in women. Other health effects such as adverse pregnancy and eye diseases are equally common [9, 10].
To better appreciate the health effects of fuelwood combustion, a review of literature for over 17 papers was done. The objective was to capture the most prevalent health outcomes as a result of indoor and outdoor fuelwood combustion. The table below (Table 1) shows the results of the reviewed papers in a summary form [11].
Author(s) | Study carried out | Country/region | Methodology | Major findings | Discovered health outcomes |
---|---|---|---|---|---|
Bruce et al. [12] | Indoor biofuel air pollution and respiratory health: the role of confounding factors among women in highland Guatemala | Guatemala | Cross-sectional analysis | The prevalence of reported cough and phlegm was significantly high among women using open fire | Cough and phlegm |
Vinod et al. | Biomass cooking fuels and prevalence of tuberculosis in India | India | Logistic regression | Substantial prevalence of active tuberculosis in person living in households using biomass cooking fuels (wood or dung) | Tuberculosis |
Neelam et al. [13] | Indoor air pollution from biomass combustion and its adverse health effects in central India: an exposure response study | India | Qualitative analysis with the use of peak expiratory rate flow | Exposure to biomass smoke causes health related problems | Eye irritation, headache, bronchitis, cataract and respiratory problems |
Rajiv [14] | Disease burden of fuelwood combustion pollutants in rural households of the Himalayas | India | Qualitative analysis | The use of fuelwood has an impact on health | Acute lower respiratory infection, chronic obstructive pulmonary disease and lung cancer |
Zschauer [15] | Households’ energy supply and the use of fuelwood | Kenya (case of Taita Hills) | A qualitative analysis | The use of fuelwood has a negative impact on health | Eye problem, respiratory problems (coughing irritation of the lungs and sniffles), tuberculosis |
Silwal and McKay [16] | The impact of cooking with firewood on respiratory health | Indonesia | A unique Indonesian household survey | Individuals living in households that cook with firewood have lower lung capacity than those that cook with cleaner fuels; impact being larger on women and children | Lower lung capacity |
Agrawal and Yamamoto [17] | Effect of indoor air pollution from biomass and solid fuel combustion on symptoms of preeclampsia/eclampsia in Indian women | India | Logistic regression | Women living in households using biomass and solid fuels have two times higher likelihood of reporting preeclampsia/eclampsia symptoms than those living in households using cleaner fuels | Preeclampsia/eclampsia |
Sharma et al. [18] | Types of cooking stove and risk of acute lower respiratory infection among under five children: a cross sectional study in Rasuwa, Nepal | Nepal | Cross-sectional survey | The presence of acute lower respiratory infection among users of biomass fuels | Lower respiratory infections |
Zoë et al. | Residential heating with wood and coal: health impacts and policy options in Europe and North America | Europe and North America | Qualitative analysis | Evidence links emissions from wood and coal heating to serious health effects | Respiratory and cardiovascular mortality and morbidity |
Tao et al. [19] | Residential solid fuel combustion and impacts on air quality and human health | Mainland China | A qualitative analysis | Evidence showed that household air pollution in china causes a range of health outcomes | Respiratory diseases; asthma, lung cancer, cardiovascular diseases; heart diseases, stroke and immune system impairment |
Zidago & Wang [20] | Charcoal and fuelwood consumption and its impacts on environment | Cote d’Ivoire (Case Study of Yopougon Area) | A qualitative analysis through the households was used | The production and consumption of charcoal have a negative impact on the health of its producers and consumers | Burns, chronic cough |
Das et al. [21] | Biomass cooking fuels (firewood, Charcoal) and health outcomes for women in Malawi | Malawi | Cross-sectional sample analysis | The use of firewood and charcoal for cooking is associated with five categories of health outcomes. | cardiopulmonary, respiratory, neurologic, eye health and burns |
Kadafa et al. [22] | The health impact of fuelwood Utilisation on users | Nigeria (case study of Yelwa Village) | A qualitative analysis | It was discovered that fuelwood has health implications on its users | Eye problem, respiratory problem and heart diseases |
Mbanya and Sridhar [23] | PM10 emissions from cooking fuels in Nigerian households and their impact on women and children | Ibadan, Nigeria | Qualitative analysis | Majority of the respondents complained of health-related issues during and after cooking with cough, breathing problems, skin and eyes irritation being the most common | Cough, breathing problems, skin and eyes irritation |
Badamassi et al. [24] | The effects of particulate matter (PM2.5) from household combustion on life expectancy in SSA | 43 SSA countries | GMM and panel cointegration model | Household particulate matter (PM2.5) is significantly and negatively associated with higher life expectancy in the long run especially with females | Lower life expectancy |
Mohapatra et al. [25] | Health impact on women using solid cooking fuels in rural area of Cuttack District, Odisha | Cuttack District, Odisha | Cross-sectional study with the use of Chi-square test | Exposure to smoke from cooking fuel is significantly associated with the prevalence symptoms of headache, dry cough and hypertension | Headache, dry cough and hypertension |
Review of health effects of fuelwood combustion on users.
The exposure to smoke due to cooking fuel accelerates respiratory-related illnesses such as dry cough and nose irritation; further analysis equally underscores high association with headache, dry cough and hypertension [25]. The review above shows significant health effects related to respiratory-related diseases, of the 17 studies, 13 underscore respiratory tract infections as major outcome of fuelwood combustion. The most common forms of the respiratory tract infections are dry cough, breathing problems, neurologic problems, cardiopulmonary, cardiovascular diseases, asthma and lung cancer [20, 23, 25].
Lower life expectancy has equally been reported by Badamassi et al. [24]; they underscore that combustion of particulate matter (PM2.5) has adverse effect on life expectancy in the long run, with a greater negative effect on female life expectancy. Their study equally shows higher life expectancy for exposed households in urban areas and countries with higher GDP per capita; this can be explained by the fact that these groups can have better access to health care. Cardiovascular diseases have equally been reported to be associated with fuelwood combustion [19]. Other diseases such as asthma, stroke and immune system impairment have equally been attributed to indoor and outdoor pollution as a result of fuelwood combustion [19].
The precedent section underscores that about 700 million (82%) of Africans are at high risk of household air pollution due to the use of solid-fuel for cooking with an average 581,000 deaths annually [26]. Globally, the demand for solid-fuel for cooking has reduced considerably, average 50–40%; however, Africa stagnates at 80% over the decades. Escalating fuel cost, population growth and supply interruptions have accounted for reduced demand in modern fuel demand. Even when households use modern fuel for cooking, they often combine with solid-fuel cooking stoves [27, 28]. The production of energy from biological waste using modern production techniques has been promoted as a way out of this public health crisis. Significant efforts have been made through different cross-country projects aimed at producing clean and modern bioenergy such as liquid and gel biofuels. Efforts to promote more clean energy sources such as ethanol stoves and clean cooking stoves have not met required objectives due to poor market penetration and high subsidisation cost [26]. However, in West Africa, ethanol businesses have registered steady growth with over 200,000 stoves reported in different countries over 3 years.
Biogas projects in East Africa played an important role in changing mindsets and providing a cleaner alternative for households. In Kenya, three biogas operating units have been constructed by the Taita Taveta Wildlife Forum (TTWF) as part of a pilot project aimed at improving access to clean energy. This is promoted because biogas produces clean energy, with less indoor and outdoor pollution, thus reduced chances of respiratory tract infections and heart infections. The biogas production process equally generates nitrogen and liquids rich in nutrients that can serve as fertilizers.
The use of bioenergy as an alternative to solid-fuels is encouraged because through the different conversion techniques, energy is generated which enhances good combustion with limited emission of air pollutants. This form of energy is good both for indoor and outdoor use at urban and rural areas. The promotion of this form of energy is equally backed by the constant availability of biomass for bioenergy conversion, with by-products that are equally good for crop cultivation.
Tree commodities commonly referred to as ‘money trees’ are trees grown principally for cash by many African countries. These trees are often the principal source of income for most farmers in Sub-Saharan Africa. In Africa, cocoa, coffee, oil palm, industrial round wood, cashew, almonds and walnuts are the principal tree commodities. These tree commodities are a source of income to millions of Africans and accounts for tons of agricultural biomass produced annually. Agricultural biomass after extraction of the fruit of these products is often left to rot in the farms while farmers suffer from energy shortages. Residue from tree commodities such as husk of cocoa and coffee, empty fruit bunch of oil palm, forest thinning from timber exploitation and shell of almonds are potential sustainable feedstock for bioenergy generation.
With the appropriate technology and adoption by community members, tree commodities can serve as a pathway for sustainable bioenergy generation without changing land use and without extra efforts from the farmer to find feedstock. The potential of using bioenergy for reducing health effects of traditional biomass for combustion is backed by the fact that tree commodities are often found in rural areas, with serious energy deficiencies, high prevalence of respiratory tract infections as a result of solid wood fuel combustion.
The potential of using bioenergy from tree commodities as a clean energy source is evaporated in this chapter by looking at two aspects: (1) by evaluating the potential in terms of quantity of bioenergy that can be generated by tree commodities and (2) operational framework for bioenergy from tree commodities to effectively serve rural population as a renewable and healthy energy source.
To evaluate the potential of bioenergy from tree commodities, seven tree commodities were chosen for analysis based on the number of farmers or population affected by the different tree commodities. The chosen tree commodities are coffee (Coffea arabica and Coffea canephora), cocoa (Theobroma cacao), oil palm (Arecaceae), walnuts (Juglans), cashew (Anacardium occidentale), almonds (Prunus dulcis) and industrial round wood. When evaluating bionenergy potential from tree commodities, provisions are taken for the use of residue for other uses, such as soil nutrient. The extraction equally considers other aspects such as weather, soil types, crop yields, harvesting technique and wind patterns [29, 30]. Researchers have evaluated different soil systems and uses of residue from biomass and conclude that 44–64% of biomass residue can sustainably be used for biomass generation [29, 30, 31]. Using a more conservative approach, this chapter uses a 20% extraction rate to estimate bioenergy production from tree commodities. Data from the FAO (2018) database serve as a basis for estimation in this chapter. Sustainable extraction rates were gotten from literature review from a variety of sources; residue to product ratio and moisture content was extracted from OECD/IEA [32]. Moisture content for coffee and cocoa was obtained from NREL [33], oil palm from Husain et al. [34], walnuts from Uzan and Yaman [35], cashew from Mohod et al. [36], industrial round wood from FAO [37] and almonds from [38]. Table 2 below shows the results of bioenergy potential from tree commodities for bioelectricity, biochemical ethanol and diesel.
Tree commodity | Average | Type of residue | Residue to product ratio | Moisture content | Lower heating value | Residue (wet tons) | Residue (Bone dry tons) | Residue. 30% Sustainable Extraction (bone dry tons) | Energy potential (bone dry x MK/kg) GJ | bioelectricity | Biochemical ethanol | Thermochemical syngas to Ficher Tropsch diesel | |||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15% efficiency MW h (low) | 40% efficiency MW h (high) | (low) litres | (high) litres | Low (litres) | High (litres) | ||||||||||
Coffee | 1,149,511 | Husk | 2.10 | 0.15 | 12.56 | 2.41E+06 | 2.05E+06 | 6.16E+05 | 7.73E+06 | 3.25E+05 | 8.66E+05 | 6.77E+07 | 1.85E+08 | 4.62E+07 | 1.23E+08 |
cocoa | 3,398,572 | Pods, Husk | 1.00 | 0.15 | 15.48 | 3.40E+06 | 2.89E+06 | 8.67E+05 | 1.34E+07 | 5.63E+05 | 1.50E+06 | 9.53E+07 | 2.60E+08 | 6.50E+07 | 1.73E+08 |
oil palm | 19,402,457 | empty fruit bunch (EFB) | 0.25 | 0.60 | 15.51 | 4.85E+06 | 1.94E+06 | 5.82E+05 | 9.03E+06 | 3.79E+05 | 1.01E+06 | 6.40E+07 | 1.75E+08 | 4.37E+07 | 1.16E+08 |
Walnuts, with shell | 37,471 | shell | 2.00 | 0.15 | 16.70 | 7.49E+04 | 6.37E+04 | 1.91E+04 | 3.19E+05 | 1.34E+04 | 3.57E+04 | 2.10E+06 | 5.73E+06 | 1.43E+06 | 3.82E+06 |
Cashew | 1,713,285 | Shell | 2 | 0.15 | 23.98 | 3.43E+06 | 2.91E+06 | 8.74E+05 | 2.10E+07 | 8.80E+05 | 2.35E+06 | 9.61E+07 | 2.62E+08 | 6.55E+07 | 1.75E+08 |
Industrial round wood | 28,764,846 | forest thinning | 0.5 | 0.4 | 18.3 | 1.44E+07 | 8.63E+06 | 2.59E+06 | 4.74E+07 | 1.99E+06 | 5.31E+06 | 2.85E+08 | 7.77E+08 | 1.94E+08 | 5.18E+08 |
Almonds, with Shell | 281,549 | shell | 2 | 0.15 | 18.86 | 5.63E+05 | 4.79E+05 | 1.44E+05 | 2.71E+06 | 1.14E+05 | 3.03E+05 | 1.58E+07 | 4.31E+07 | 1.08E+07 | 2.87E+07 |
Total | 1.02E+08 | 4.26E+06 | 1.14E+07 | 6.26E+08 | 1.71E+09 | 4.27E+08 | 1.14E+09 |
Tree commodities as a source of bioenergy, bioelectricity, biochemical ethanol and diesel.
Bioenergy generation from tree commodities in Africa can potentially generate between 4.26E+06 and 1.14E+07 MW of bioelectricity from the seven-tree commodities while 6.26E+08 and 1.71E+09 L of bioethanol can potentially be generated from tree commodities. Tree commodities equally can equally serve as an important potential source for diesel, estimates from tree commodities show that 4.27E+08–1.14E+09 L can be generated from tree commodities.
The figure above (Figure 1) shows that bioenergy generation from industrial round wood is the highest averaging 46% for the bioelectricity (47%), bioethanol (46%) and Fischer-Tropsch diesel (46%). Cashew shell can equally contribute significantly bioenergy production accounting for 15% of diesel and bioethanol and 21% of electricity. Cocoa comes third as the highest contributor, accounting for 13% of bioelectricity and 15% of bioethanol and diesel. Coffee follows representing 7% of bioelectricity production and 11% of bioethanol and diesel. Oil palm equally contributes significantly to this potential, with 9% of total potential of bioelectricity and 10% of bioethanol and diesel potential production. These percentages underscore the significant potential contribution of tree commodities in generation clean, modern bioenergy than can potentially reduce public health diseases associated with the combustion of solid-fuel biomass. However, for this to be a reality, a lot of policy and operational tools must be put in place and readily available at local level.
Percentage of bioenergy generated from different tree commodities.
For modern bioenergy to serve as a potential clean energy source for rural African communities and millions of Africans at risk of respiratory tract infections and cardio-vascular diseases, several important pre-requisites are required.
Government support: For modern bioenergy to be a mainstay in rural Africa and reduce incidences of deaths through solid-fuel combustion, government authorities must support the development of modern bioenergy infrastructure. This requires significant shift in policy and investment from the government and different multi-lateral partners. The understanding of policy makers of the health advantages of developing modern bioenergy systems coupled with sustainable management practices is key to pushing a policy reform agenda for modern bioenergy generation in Africa.
Significant financial investment: Developing modern bioenergy generation systems for tree commodities requires significant financial investment. Multi-lateral development agencies aimed at reducing carbon emissions and promoting healthy living of populations can shift their funding streams to bioenergy generation. For this to happen, they must understand that modern bioenergy does not only reduce carbon emission, deforestation but can equally save the lives of millions of people potentially at high risk of respiratory tract infections as a result of solid-fuel combustion. This financing should go along way in investing not only in infrastructure for bioenergy development but equally in community adapted distribution mechanisms that will enhance adoption of new form of energy. These new energy sources should be cheaper and more efficient for adoption to be faster.
The acceptance and adoption of new bioenergy as an improved energy source required that users understand the key advantages. Thus, sensitisation at different levels with a clear distinction of advantages over traditional solid-fuel combustion should be made. Adoption can equally be facilitated by developing simple modern bioenergy generation systems that are adapted to rural context with minimal investment. This will enhance adoption especially when the cost of generation is relatively low and accrued advantages and multi-scaled.
Public-private partnerships and cooperation: The developments of sustainable modern bioenergy systems stakeholder buy-in a different levels and scales. Thus, a public-private partnership scheme is very important. The private sector with similar objectives can collaborate with government agencies in developing the bioenergy agenda as financial partners, technical support agents, or for policy advocacy. International cooperation is equally important for broad-based decision-making with local impacts coupled with strategic deployment frameworks adapted to different contexts. Understanding different stakeholders from different countries is paramount to advancing bioenergy generation.
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\\n\\nNo partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Author or any Co-Author, nor authorize any party to make or enter into any commitments for, or on behalf of, any other party.
\\n\\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
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\n\nGoverning law: This Publication Agreement and any dispute or claim, including non-contractual disputes or claims arising out of, or in connection with it, or its subject matter or formation, shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of, or in connection with, this Publication Agreement, including any non-contractual disputes or claims.
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