**Abstract**

This study investigated concentrations and spatial distributions of four heavy metals: Cadmium (Cd), Chromium (Cr), Copper (Cu), and Lead (Pb) in the soil and drainage systems resulting from informal e-waste recycling at Ashaiman, a town in the Greater Accra Region of Ghana. Twenty-four soil samples were randomly taken from two open burning sites, and three water samples from a drainage that flows through the scrapyard were digested using standard wet digestion methods. An atomic absorption spectrophotometer (AAS) was used to analyze three replicates per sampling location for the heavy metals. The results revealed that the soil and drainage samples were polluted, with the metallic levels exceeding the World Health Organization (WHO), the Food and Agricultural Organization (FAO) of the United Nations, and the Environmental Protection Agency (EPA) of Ghana limits. Geoaccumulation index (Igeo), pollution load index (PLI), and contamination factor (CF) further confirmed the contamination of the scrapyard by the heavy metals. Spatial distribution maps showed elevated levels of the heavy metals at portions designated for open burning and disposal of e-waste materials. The research corroborates studies on pollution of the environment by informal e-waste activities and underscores the urgent need for policy implementation and law enforcement to halt further pollution.

**Keywords:** e-waste recycling, heavy metals, pollution indices, spatial distribution, Ashaiman scrapyard-Ghana

### **1. Introduction**

E-waste has other names, such as waste electrical and electronic equipment (WEEE) and e-scrap. There is no universally-agreed e-waste definition in both legislation and daily usage. This has generated countless definitions in e-waste regulations, policies, and guidelines. In this paper, we adopt the non-legal definition provided by Solving the E-Waste Problem (StEP) Initiative White Paper [1], which is as follows: "E-Waste is a term used to cover items of all types of electrical and electronic equipment (EEE) and its parts that the owner has discarded as waste without the intention

of re-use." By this definition, selecting the Ashaiman scrapyard in the Greater Region of Ghana as the study area is seamlessly connected and justified. Due to their different lifespan profiles, different e-waste materials generate different volumes, potential environmental and health impacts, and economic values [2].

Owing to rapid changes in technological updates and upgrades of EEE, industrialization and modernization, an increase in disposable income, and the popularized increase in the use of EEE, there is an upsurge in the acquisition and utilization of electrical and electronic products. Consequently, e-waste generation has the world's largest and fastest growth rate. Asia contributed most to the generation of e-waste in 2019, generating close to 24.9 million Metric tons (Mt), followed by Europe (12.0 Mt), Americas (13.1 Mt), Africa (2.9 Mt), and Oceania (0.7 Mt) [2, 3].

As a result of free and illegal trading activities and the lack of implementation of environmental policies, Africa receives high quantities of potential e-waste materials from these continents. Liberia, Nigeria, Ghana, Benin, and Ivory Coast are major destinations for these "slightly used" materials [4]. Around 600,000 used EEE were imported into Nigeria in 2010. Also, close to 30% of second-hand imports into the country were considered non-functioning and thus regarded as e-waste. Ghana's e-waste quantities rose from 63,000 tons per year in 2003 to 169,000 tons per year in 2008, with a further increment to 215,000 tons in 2009. Only 30% of the total electrical appliances that arrived in Ghana in 2009 were determined to be new, with the rest regarded as used products, 15% of which were either faulty or outmoded and thus could not be sold, eventually ending up in the informal recycling sector [5].

However, e-waste materials in Ghana or Africa need to be better managed due to ignorance on the part of the public on the dangers of poor disposal systems, lack of safe systems of disposal, and absence of government policy and legislation or the enforcement of same. E-waste management in Ghana, just like in most African countries, is managed by the unhindered and poorly equipped informal sector [1]. Manual dismantling, acid leaching, open burning, and indiscriminate disposal of e-waste material are usually the methods of choice in the informal sector to recover valuable metals such as Cu, Au, and Ag that can be resold. These actions release toxic substances, including Polycyclic Aromatic Hydrocarbons, Organochlorine compounds, Phthalates, and heavy metals [2, 6]. The release of these compounds results in atmospheric pollution, and a reduction in the physicochemical characteristics of water quality, including pH, phosphate, oxygen, and chloride levels. Soil composition and viability to support plant life are compromised severely following e-waste contamination [7–9]. Essentially, poor e-waste management has a negative outlook on the realization of the Sustainable Development Goals (SDGs), particularly Goals 3 (Good health and wellbeing), 6 (Clean water and sanitation), 8 (Decent work and economic growth), 11 (Sustainable cities and communities), 12 (Responsible consumption and production), and 14 (Life below water) [1].

Heavy metals are significant components of e-waste materials. The application of these metals in electrical gadgets is influenced by good electrical conductivity to minimize power losses, an inert environment in operations to ensure reliable functioning, and using metals compatible with manufacturing processes [10]. Heavy metals make up about 60.2% of significant constituents of e-waste, including elements such as Tin (Sn), Mercury (Hg), Antimony (Sb), and Arsenic (As) [11]. The parent circuit board of many electrical gadgets houses heavy metals like As, Cd, Pb, and Hg [2]. For example, Pb constitutes nearly 0.4–1.0 kg of the total mass of cathode ray tubes found in computer monitors and television sets, respectively. Also, personal

### *Heavy Metal Pollution Resulting from Informal E-Waste Recycling in the Greater Accra Region… DOI: http://dx.doi.org/10.5772/intechopen.112397*

desktop computers (which weighed approximately 32 kg) contain Pb (6.3%), Cu (6.9%), Cobalt (Co) (0.02%), and Iron (Fe) (20.5%) [2, 11].

Heavy metals released during informal e-waste recycling are absorbed into living tissues, usually through inhaling toxic fumes and particulate matter and ingesting contaminated food and water [6]. Cadmium (Cd) is a known carcinogen of the lungs, kidneys, and prostate. Exposure to Cr causes cardiovascular diseases, hematological and neurological effects, and sometimes even death. Pollution resulting from Pb induces memory loss, dullness, anemia, convulsions, tremors, headache, and irritability, while respiratory irritation such as coughing and sneezing, gastrointestinal effects, including nausea, anorexia, diarrhea, and hematological effect result from exposure to Cu [12–15].

The informal sector dominates e-waste recycling activities in Ghana. This is mainly due to the need for more implementation of environmental-related laws and poverty. It is estimated that between 121,800 and 201,600 individuals are involved in the informal e-waste sector in Ghana. The formal recycling sector in Ghana receives only about 0.2% of e-waste for treatment [16]. Unsurprisingly, Ghana is noted for having one of the most significant e-waste recycling in Africa, at Agbogbloshie in Accra, Ghana.

This research assessed the pollution levels and spatial distributions of four heavy metals (Cd, Cr, Pb, and Cu) at two burning sites within the Ashaiman scrapyard in the Greater Accra Region of Ghana, where informal e-waste recycling occurs. Pollution levels of the heavy metals were investigated using selected pollution and contamination indices. The spatial distributions were also investigated using inverse distance weighted (IDW) method. The rest of the chapter is structured as follows: Section 2 describes Materials and Methods; Section 3 focuses on the Results. One of the key findings is that soil and drainage systems of the Ashaiman scrapyard were polluted with Cu, Pb, Cr, and Cd, mainly due to open burning and dumping of e-waste materials; Section 4 is devoted to Discussion; and Section 5 presents the Conclusions of the study.

## **2. Materials and methods**

The Ashaiman scrapyard is located at the entry into the township from the Tema metropolis, about 0.12 km from the Accra-Tema Motorway. Covering a land size of about 0.07 km2 , it is located on latitude 5o 41′4.99″ N and longitude 0o 01′37.28″ W. The region is generally flat, with savannah grasses and shrubs being the dominant vegetation. The topsoil is primarily sandy clay, with the subsoil predominantly clay [17, 18].

The scrapyard houses large metal containers, which store e-waste materials until they are ready to be worked on. Dismantling and sorting activities were performed in sheds and wooden structures at sections of the scrapyard. Burning of e-waste to isolate valuable metals was done on the open field, though few burning activities were observed at the dismantling and sorting areas. At the time of research, two main sites were identified where open burning occurred. E-waste materials in the scrapyard included refrigerators, television sets, computers, cables, radios, and incandescent light bulbs.

Located very close and separating the scrapyard from a dumpsite is a drain that flows from the northern end of the scrapyard and serves as irrigation for farming crops and drinking water for herds of cattle.

### **2.1 Sample collection**

### *2.1.1 Soil samples*

Two burning sites (F and H) were chosen for soil sampling. Site F is located at the central portion of the scrapyard. Major parts of this site were used for the open burning of e-waste, though a few sections served as dumping grounds for e-waste material after dismantling, sorting, and burning. Site H lay closer to the drain running through the scrapyard. This site was used both for open burning and dumping of e-waste materials. **Figure 1** shows a map of the scrapyard area.

Samples were taken in the early hours of 16 July 2019. At each burning site, five topsoil samples (marked 1A, 2A, 3A, 4A, and 5A) within a soil depth of 0–10 cm and five subsoil samples (marked as 1B, 2B, 3B, 4B, and 5B) within a depth profile of 10–20 cm, were randomly collected from different sections. Thus, 10 soil samples were taken from each burning site, and 20 samples were obtained from the two burning areas. Four other topsoil samples were taken at distances of 25 m, 50 m, 75 m, and 100 m from the scrapyard (marked as HV 20, HV 50, HV 75, and HV 100, respectively) to test the detection of heavy metals as one moved away from the scrapyard. Sampling was done with a newly purchased stainless-steel garden shovel and a standard measuring rule to determine the vertical depth of the soil profile. The coordinates at sampling points were recorded using GPS software. A map showing the sampling points of the soil samples is shown in **Figure 2**.

**Figure 1.** *Map showing scrapyard at Ashaiman.*

*Heavy Metal Pollution Resulting from Informal E-Waste Recycling in the Greater Accra Region… DOI: http://dx.doi.org/10.5772/intechopen.112397*

**FIgure 2.** *Aerial view of the sampling points at the scrapyard.*

### *2.1.2 Water samples*

Three water sediment samples were collected about 140 m north of the scrapyard and mixed to form the control sample (WS C). Within the scrapyard, a water sediment sample was collected (WS 1), about 370 m from the control sample. In contrast, a second sample (WS 2), purely water without sediment from the drain, was also obtained, about 30 m from the second water sediment sample. Coordinates were taken using GPS software. pH of these samples was taken on-site using a Hanna pH meter calibrated with buffer solutions of pH 4, 7, and 10.

Samples were collected into plastic bowls with tightly fitting lids pre-cleaned with nitric acid and sent to the Ghana Standards Authority for treatment and analysis.

### **2.2 Soil sample preparation and determination of pH**

Soil samples were air dried at around 105°C to eliminate wetness and obtain only constant weights representing the soil. They were then passed through a 2 mm non-metallic mesh to separate and remove rocks exceeding 0.25 inches (6.35 mm). Manual milling with mortar and pestle thoroughly homogenized the soil particles passing the mesh. These preparations were necessary for good dissolution during chemical treatments

to increase the accuracy of the analysis [19]. To 3 g of each of the dried and sieved soil samples in a 25 ml beaker (which had been pre-cleaned and thoroughly washed with distilled water), 15 ml of aqua regia was added, and the resulting solution digested in a fume chamber for about 30 minutes to remove foreign materials that might interfere with the analytical test. Following cooling, distilled water was added to the digested sample and filtered into a 100 ml volumetric flask using the Johnson test paper filter paper with a diameter of 125 mm. Distilled water was added to the solution to the 100 ml mark.

Soil samples were prepared for pH analysis by dissolving 2 g of each sample in distilled water in a 1:1 ratio and stirring to a uniform suspended mixture using a clean glass rod. The samples were then allowed to settle for about 10 minutes. The samples were continually stirred for about 15 minutes using a magnetic stirrer on a magnetic sitter plate. The samples were allowed to settle, and their pH was determined by a handheld Hanna pH meter calibrated with pH buffer solutions 4, 7, and 10 [20, 21].


**Preface to "Life Satisfaction and Psychological and Physical Well-Being"**

The literature has seen a tremendous upsurge of interest in concepts such as life satisfaction, happiness, subjective well-being, and quality of life. These concepts are not just of interest from an academic point of view, but there is great interest in tracking indices pertaining to happiness as indicated by the publication of The World Happiness Report annually since 2012, a publication of the "Sustainable Development Solutions Network, powered by the Gallup World Poll data" (https: //worldhappiness.report/faq/).

In March 2021, we invited scholars from around the world to contribute to a Special Issue of the *International Journal of Environmental Research and Public Health* on life satisfaction and the related aspects of psychological and physical well-being. The effort resulted in 14 articles from various countries: China, Sweden, Norway, France, Peru, New Zealand, the USA, and Italy. All articles, except one, are based on empirical studies. One article is a systematic review of literature on adolescents and reviews literature from many countries. Only one article is based on an experimental study. The samples in these studies reflect wide diversity: university students and faculty, nurses, entrepreneurs, adolescents, national databases, refugees, and community samples recruited through social media. We hope that this collection of articles, being issued in a book form, will help stimulate work internationally.

> **V. K. Kumar and Jasmin Tahmaseb-McConatha** *Editors*

#### *Article* **The Role of Stress Experience and Demographic Factors for Satisfaction with Life in Norwegian Adolescents: Cross-Sectional Trends over a Ten-Year Period**

**U. K. Moksnes 1,\*, S. T. Innstrand 2, M. Lazarewicz 3 and G. A. Espnes 1**

**Abstract:** Background: The individual's perception of life satisfaction (LS) is regarded as a key indicator of one's overall experience of wellbeing, sensitive to the broad spectrum of functioning. Adolescence is particularly an important period for assessing LS and factors associating with LS. The present study investigated cross-sectional trends in adolescents' LS levels across three time points over a 10-year period, as well as the role of stress experience and socio-demographic differences in association with LS. Methods: The study used cross-sectional data from three time points: 2011 (n = 1239), 2016 (n = 1233), and 2022 (n =311), including adolescents from lower and upper secondary public schools, with an age range of 13–20 years. Results: There were relatively high and stable mean scores on LS across all time points; however, significant differences were found between 2011 and 2016. Results from the multivariate linear regression analysis showed that sex and age were moreover weakly associated with LS, where LS decreased slightly between the ages of 13 and 18 years and increased from 19 to 20 years. Of the stress domains, interpersonal and school-related stressors showed the strongest negative and significant association with LS; significant interaction effects of sex by stress domains were found, but not with sex by time. Conclusion: This study supports the relatively high and stable level of LS in adolescents across the investigated time points. Demographic factors were moreover weakly associated with LS. The findings also contribute by showing the significant role of interpersonal and school-related stressors in association with LS.

**Keywords:** wellbeing; life satisfaction; youth; adolescence; normative stressors; interpersonal stressors; school stressors


*2.1.2 Material source*

As the name suggests, biologic skin substitutes are made using biological materials from human or animal sources. Synthetic material refer to non-biological materials engineered in a laboratory to replace or support regeneration of one or more components of normal skin structure. Biosynthetic skin substitutes are those made using a combination of biological and synthetic materials. Skin substitutes with biological components may allow replication of a more natural neodermal structure generated by native extracellular matrix and can allow excellent re-epithelialisation, although staged procedures are often necessary due to slow vascularisation. Synthetic skin substitutes offer increased control over scaffold composition and less propensity to loss due to cross-species antigenicity or infective complications [16] as compared with materials with biological origins, following integration with the burn wounds to which they applied.

### *2.1.3 Layering*

Single layered skin substitutes replace either the epidermal (e.g., Suprathel) or dermal (e.g., Matriderm) component of skin, the latter of which requires autologous skin grafting to complete the epidermal reconstruction. Bilayered dermal substitutes are designed to replace or replicate both dermal and epidermal layers during their application whether this is on a temporary basis (e.g., Biobrane) or a permanent basis (e.g., Integra, BTM). In the latter case a two-stage approach is required whereby the impermeable pseudo-epidermal layer is removed or 'delaminated' prior to autologous skin graft application for definitive wound closure. A two staged strategy is particularly advantageous in major burn patients by allowing autologous skin harvest to be deferred until such time as the patient has reached a point of physiological stabilisation, resolution of inhalational injury or other concomitant traumatic injury [12] and optimisation of cardiac or other medical comorbidities [17]. This approach also facilitates re-epithelialisation of donor site harvest for repeated harvest in major burn patients who may have a paucity of donor site due to their total body surface area of burn, while permitting mobilisation during the integration phase [18].


*2.3. Filtering Steps*

As noted in Figure 1, some of the false positives generated by the ensemble of classifiers are extracted by applying several filtering approaches that take advantage of the depth maps. The filters tested in this work are the set of six tested in [9] (viz. SIZE, STD, SEG, ELL, EYE, and SEC) and a new filter proposed here (viz. WAV), which is based on processing the image with different wavelets. Each of these filtering techniques is described below. Figure 3 illustrates images rejected by the seven types of filters.

**Figure 3.** Examples of images rejected by the different filtering methods.


**3. Proteomic research in caprine milk**

Milk is composed of three main components: casein, whey proteins, and milk fat globule membrane (MFGM). Using differential centrifugation and ultracentrifugation, it is possible to isolate these three fractions. While the application of proteomics in the milk of larger animals has attracted many research groups, the role of proteomics in the milk of goat remained limited. In recent years, with the advancements of available technologies in proteomics, there has been a growing interest to unravel the dynamic structure of goat milk protein contents. Proteomic research in caprine milk has been applied in the areas of identifying major proteins, comprehensive analysis, MFGM, and identification of PTM (**Table 1**).

**3.1. Identifying major proteins**

assay; MRM, multiple-reaction-monitoring.

**Table 1.** Proteomic investigations of caprine milk.

1

Affinity

chromatography & nLC-Chip-QTOF-MS

protein in many ruminants including goat.

components can be detected.

One of the first proteomic studies in goat milk was reported by Roncada et al., where they used two dimensional electrophoresis (2-DE) combined with MALDI-TOF and ESI-ion trap mass spectrometers to analyze αS1-casein alleles. They determined casein polymorphisms as the key characteristics in the cheese manufacturing industry [24]. Major goat milk proteins including caseins (αS1, αS2, β-, and κ-casein) and some of the whey proteins (albumin, lactoferrin, β-lactoglobulin, and α-lactalbumin) are highly abundant and have been widely studied [8, 25, 37]. Several groups compared the data from proteomics studies of goat milk with those of cow and other species, but their reports involved only the characterization of major proteins [27, 36, 41]. In one of 2-DE analyses [36], highly abundant proteins in each animal species display their own unique pattern [36]. This group further highlighted significant interspecies differences in milk from different ruminants and identified β-lactoglobulin as the major whey

nLC-MS/MS MFGM N-glycosylation comparison in

LC-MS/MS MFGM Phosphoproteome analysis [48]

Literature references of each study: 2-DE, two-dimensional; 1-DE, one-dimensional; nLC, nano liquid chromatography; UPLC, ultra-performance liquid chromatography; SCX, strong cation exchange; CPLL, combinatorial peptide ligand libraries; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis; ELISA, enzyme-linked immunosorbent

**Techniques Matrix Proteomic study References1**

Skim milk Lactoferrin N-glycans in

phosphoproteome

human and bovine

mammals

[29]

Proteomic Analysis of Goat Milk

149

http://dx.doi.org/10.5772/intechopen.70082

[47]

[46]

PTM 1DE & nLC-MS/MS Skim milk Comprehensive caseins

The use of goat milk as a substitute for cow milk for allergic people has been also recently reported [3, 10]. In some parts of the world, milk of donkey or goat is used in newborn and infant feeding because they are less allergenic than cow milk. Accordingly, Di Girolamo et al. used fingerprinting of major milk proteins by MALDI-TOF MS coupled to a robust statistical analysis to determine adulteration and unintended contamination of donkey milk and goat milk [26]. Peptide mass fingerprinting is a simple methodology in proteomics where proteins are cleaved with a protease, such as trypsin. The identification is accomplished by matching the observed peptide masses from MS data to the theoretical masses derived from a sequence database [49]. Comparative proteomic approaches were used to study colostrum and milk of goats, cows, and sheep to determine chemical composition and immunoglobulin concentration [39]. The study revealed that despite the similar immunoglobulin concentrations in colostrum and milk from the three studied species, differences in several immune

1 Literature references of each study: 2-DE, two-dimensional; 1-DE, one-dimensional; nLC, nano liquid chromatography; UPLC, ultra-performance liquid chromatography; SCX, strong cation exchange; CPLL, combinatorial peptide ligand libraries; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis; ELISA, enzyme-linked immunosorbent assay; MRM, multiple-reaction-monitoring.

**Table 1.** Proteomic investigations of caprine milk.

#### **3.1. Identifying major proteins**

**Techniques Matrix Proteomic study References1**

adulteration

proteins

LPS induced

Brazilian breeds

allergic properties

proteins/adulteration

quantification

quantification

abundance proteins

(MRM) Whey powder

Milk Comprehensive of low

Milk Comparison study sheep and goat

composition

biological activity

proteins

[35]

[36]

[28]

[37]

[3]

[26]

[38]

[39]

[40]

[41]

[6]

[42]

[8]

[44]

Major proteins 2-DE & MALDI-TOF Milk Casein profile [24]

nLC-MS/MS Cheese Comparative proteomics/

2-DE & MALDI-TOF Skim milk Farm animals principle

2-DE & nLC-MS/MS Skim milk Comparison of healthy and

1DE/2-DE/HPLC Skim milk Comparative proteomics

MALDI-TOF Skim milk Anti-inflammatory/anti-

MALDI-TOF-MS Skim milk Fingerprinting of principle

MALDI-TOF-MS Skim milk Fingerprinting of major

UPLC-XevoTQS Milk powder Absolute quantification

Comprehensive SCX & nLC-MS/MS Whey Farm animals comparative &

MGFM 1D SDS/PAGE MFGM Protein composition of MFGM [43]

1DE &MALD-TOF MFGM Assessment of protein

LC-MS/MS MFGM Proteome profile and

nLC-MS/MS MFGM Colostrum & mature milk [45]

2-DE/nLC-MS/MS MFGM Quantification of mammalian [46]

CPLL, 1DE & nLC-MS/MS

148 Goat Science

2-DE & MALDI-TOF & LC-MS/MS

2-DE/ELISA Colostrum/skim milk IgG/IgM bindings profile &

1-DE & LC-MS/MS Skim milk Comparative proteomics [27]

MALDI-TOF Raw milk Comparative proteomics [25]

One of the first proteomic studies in goat milk was reported by Roncada et al., where they used two dimensional electrophoresis (2-DE) combined with MALDI-TOF and ESI-ion trap mass spectrometers to analyze αS1-casein alleles. They determined casein polymorphisms as the key characteristics in the cheese manufacturing industry [24]. Major goat milk proteins including caseins (αS1, αS2, β-, and κ-casein) and some of the whey proteins (albumin, lactoferrin, β-lactoglobulin, and α-lactalbumin) are highly abundant and have been widely studied [8, 25, 37]. Several groups compared the data from proteomics studies of goat milk with those of cow and other species, but their reports involved only the characterization of major proteins [27, 36, 41]. In one of 2-DE analyses [36], highly abundant proteins in each animal species display their own unique pattern [36]. This group further highlighted significant interspecies differences in milk from different ruminants and identified β-lactoglobulin as the major whey protein in many ruminants including goat.

The use of goat milk as a substitute for cow milk for allergic people has been also recently reported [3, 10]. In some parts of the world, milk of donkey or goat is used in newborn and infant feeding because they are less allergenic than cow milk. Accordingly, Di Girolamo et al. used fingerprinting of major milk proteins by MALDI-TOF MS coupled to a robust statistical analysis to determine adulteration and unintended contamination of donkey milk and goat milk [26]. Peptide mass fingerprinting is a simple methodology in proteomics where proteins are cleaved with a protease, such as trypsin. The identification is accomplished by matching the observed peptide masses from MS data to the theoretical masses derived from a sequence database [49]. Comparative proteomic approaches were used to study colostrum and milk of goats, cows, and sheep to determine chemical composition and immunoglobulin concentration [39]. The study revealed that despite the similar immunoglobulin concentrations in colostrum and milk from the three studied species, differences in several immune components can be detected.

Proteomics is instrumental in detecting milk adulteration as adulteration has been a significant problem in the dairy industry. In a comparative proteomic study, different cheese samples obtained from milk of cow, sheep, and goat were analyzed using HPLC-chip-MS/ MS [35]. The authors found κ-casein to have a unique primary structure and suggested that it could be used to determine the origin of milk in different cheese samples. In another study, a MALDI-TOF-MS platform was used to profile milk samples for the rapid detection of illegal adulterations caused by the addition of either nondeclared cow milk, milk of other species, or the addition of powdered milk to the fresh counterpart [38]. For this purpose, peptide and protein markers of cow, water buffalo, goat, and sheep milk were identified and the effects of thermal treatment-associated adulterated milk samples were evaluated. This study introduced an independent, complementary peptide profiling measurement and extended proteomic approaches to the analysis of thermal treatment. Yang et al. analyzed milk whey samples obtained from a number of species including goat, cow, buffalo, yak, and camel. They detected certain proteins as the characterizing traits for a given species that could be used to evaluate adulteration [41].

when cow milk was added to sheep or goat milk [40]. In this study, two peptides derived from β-lactoglobulin were chosen as the protein markers. Similar isotopically labeled peptides as internal standards were designed and synthesized to minimize the matrix effect, which led to more accurate quantification results. MRM has been used for many years to measure and quantify small molecules, drugs, and metabolites. However, the application of MRM to obtain absolute quantitation of proteins is relatively new and offers great potential

Proteomic Analysis of Goat Milk

151

http://dx.doi.org/10.5772/intechopen.70082

Goat milk is a great source of essential fatty acids, which are concentrated in MFGM along with a complex mixture of proteins, glycoproteins, and enzymes [52]. Although MFGM proteins account for a small fraction of the total milk protein, they have been extensively studied in human and bovine milk. In previous bovine MFGM studies, many health benefits including anticancer, antimicrobial, and antiviral effects have been attributed to the glycoproteins [53].

The protein composition of goat MFGM has been described by several groups [8, 43, 44, 53]. Cebo et al. used 1DE and MALDI-TOF-MS and reported butyrophilin, lactadherin, mucin, and lectin as major MFGM proteins in goat milk. Interestingly, lactadherin from goat milk consisted of a single polypeptide chain whereas 2 polypeptide chains were detected in bovine milk [8]. In addition, the MFGM of colostrum and mature milk was also investigated [45]. As expected, the acute phase proteins were higher in colostrum MFGM, signifying the impor-

In a more recent 2016 study by Yang et al., the N-glycoproteome of MFGMs, obtained from a group of mammals' milk including human, Holstein, Jersey, buffalo, yak, camel, horse, and goat was investigated. They found that protein components of MFGM fractions from ruminants were more similar to each other when compared to nonruminants [46]. In a comprehensive analysis of goat milk MFGM, Henry et al. reported the use of high-resolution LC-MS/MS to expand the MFGM proteome in goat milk to 442 functional groups [48]. The main focus of their study was to probe the phosphoproteome of goat MFGM that will be

PTMs are chemical modifications that play a key role in functional proteomics. The characterization of PTMs, although challenging, is very important as they regulate protein function and control numerous important biological processes. A large number of different PTMs have been reported, but by far, phosphorylation and glycosylation are the most important and well-studied [54]. Since phosphorylation has a low stoichiometry, there is a need for enrichment of phosphopeptides in an analysis of a complex mixture. The current method to enrich phosphopeptides is based on affinity purification using phosphospecific antibodies immobilized-metal affinity chromatography (IMAC). Zhong et al. optimized the selective isolation of mono- and multi-phosphorylated peptides by using different forms of iron ions [55].

Therefore, there is a need to characterize this important component in goat milk.

tance of colostrum intake to the immune system of newborns.

to the field.

**3.3. Analysis of MFGM**

covered in the next section.

**3.4. Post-translational modifications (PTM)**

#### **3.2. Comprehensive analysis**

Proteomic investigation is challenging due to the wide dynamic range of protein expression where the presence of high abundance protein masks or prevents the detection of low abundance proteins. Recently, the most comprehensive proteomic dataset of goat milk has been reported by Cunsolo et al. [6]. This group fractionated the total milk samples using combinatorial hexapeptide ligand libraries (CPLL; such as ProteoMiner) at different pH levels to reduce the dynamic range of protein concentrations. They identified 452 unique gene products including many low abundance proteins in goat milk. Their success was also related to the use of further fractionation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), high resolution, nLC-MS/MS, and an advanced bioinformatics platform [6]. New strategies employing multi-enzyme digestion coupled with CID and ETD for protein sequencing and characterization was also used to increase sequence coverage and localization of PTMs [50]. In a separate study, using two complementary proteomic approaches, Anagnostopoulos et al. investigated the milk whey of three Greek sheep and goat breeds. They identified about 600 protein groups, most of which were involved in nutrient transport and immune system responses [42]. These findings provide the most comprehensive description of the goat milk proteome that can be utilized to build a goat protein sequence database. As it was pointed out by Soares et al. the success of proteomic-based investigations largely depend on the availability of complete and annotated databases containing the gene and protein sequence information for different animal species [51]. Hence, the data generated from these recent, more comprehensive proteomics analysis of the goat milk proteome could facilitate the detection and characterization of additional milk proteins in future proteomics analyses of goat milk.

While proteomic analyses have been used to qualitatively identify adulterants in milk, no reliable, selective, and sensitive method existed to obtain absolute quantification. In a clever analysis, Chen et al. used multiple reaction monitoring (MRM) to quantify milk adulteration when cow milk was added to sheep or goat milk [40]. In this study, two peptides derived from β-lactoglobulin were chosen as the protein markers. Similar isotopically labeled peptides as internal standards were designed and synthesized to minimize the matrix effect, which led to more accurate quantification results. MRM has been used for many years to measure and quantify small molecules, drugs, and metabolites. However, the application of MRM to obtain absolute quantitation of proteins is relatively new and offers great potential to the field.

#### **3.3. Analysis of MFGM**

Proteomics is instrumental in detecting milk adulteration as adulteration has been a significant problem in the dairy industry. In a comparative proteomic study, different cheese samples obtained from milk of cow, sheep, and goat were analyzed using HPLC-chip-MS/ MS [35]. The authors found κ-casein to have a unique primary structure and suggested that it could be used to determine the origin of milk in different cheese samples. In another study, a MALDI-TOF-MS platform was used to profile milk samples for the rapid detection of illegal adulterations caused by the addition of either nondeclared cow milk, milk of other species, or the addition of powdered milk to the fresh counterpart [38]. For this purpose, peptide and protein markers of cow, water buffalo, goat, and sheep milk were identified and the effects of thermal treatment-associated adulterated milk samples were evaluated. This study introduced an independent, complementary peptide profiling measurement and extended proteomic approaches to the analysis of thermal treatment. Yang et al. analyzed milk whey samples obtained from a number of species including goat, cow, buffalo, yak, and camel. They detected certain proteins as the characterizing traits for a given species that could be

Proteomic investigation is challenging due to the wide dynamic range of protein expression where the presence of high abundance protein masks or prevents the detection of low abundance proteins. Recently, the most comprehensive proteomic dataset of goat milk has been reported by Cunsolo et al. [6]. This group fractionated the total milk samples using combinatorial hexapeptide ligand libraries (CPLL; such as ProteoMiner) at different pH levels to reduce the dynamic range of protein concentrations. They identified 452 unique gene products including many low abundance proteins in goat milk. Their success was also related to the use of further fractionation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), high resolution, nLC-MS/MS, and an advanced bioinformatics platform [6]. New strategies employing multi-enzyme digestion coupled with CID and ETD for protein sequencing and characterization was also used to increase sequence coverage and localization of PTMs [50]. In a separate study, using two complementary proteomic approaches, Anagnostopoulos et al. investigated the milk whey of three Greek sheep and goat breeds. They identified about 600 protein groups, most of which were involved in nutrient transport and immune system responses [42]. These findings provide the most comprehensive description of the goat milk proteome that can be utilized to build a goat protein sequence database. As it was pointed out by Soares et al. the success of proteomic-based investigations largely depend on the availability of complete and annotated databases containing the gene and protein sequence information for different animal species [51]. Hence, the data generated from these recent, more comprehensive proteomics analysis of the goat milk proteome could facilitate the detection and characterization of additional milk proteins in future proteomics

While proteomic analyses have been used to qualitatively identify adulterants in milk, no reliable, selective, and sensitive method existed to obtain absolute quantification. In a clever analysis, Chen et al. used multiple reaction monitoring (MRM) to quantify milk adulteration

used to evaluate adulteration [41].

**3.2. Comprehensive analysis**

150 Goat Science

analyses of goat milk.

Goat milk is a great source of essential fatty acids, which are concentrated in MFGM along with a complex mixture of proteins, glycoproteins, and enzymes [52]. Although MFGM proteins account for a small fraction of the total milk protein, they have been extensively studied in human and bovine milk. In previous bovine MFGM studies, many health benefits including anticancer, antimicrobial, and antiviral effects have been attributed to the glycoproteins [53]. Therefore, there is a need to characterize this important component in goat milk.

The protein composition of goat MFGM has been described by several groups [8, 43, 44, 53]. Cebo et al. used 1DE and MALDI-TOF-MS and reported butyrophilin, lactadherin, mucin, and lectin as major MFGM proteins in goat milk. Interestingly, lactadherin from goat milk consisted of a single polypeptide chain whereas 2 polypeptide chains were detected in bovine milk [8]. In addition, the MFGM of colostrum and mature milk was also investigated [45]. As expected, the acute phase proteins were higher in colostrum MFGM, signifying the importance of colostrum intake to the immune system of newborns.

In a more recent 2016 study by Yang et al., the N-glycoproteome of MFGMs, obtained from a group of mammals' milk including human, Holstein, Jersey, buffalo, yak, camel, horse, and goat was investigated. They found that protein components of MFGM fractions from ruminants were more similar to each other when compared to nonruminants [46]. In a comprehensive analysis of goat milk MFGM, Henry et al. reported the use of high-resolution LC-MS/MS to expand the MFGM proteome in goat milk to 442 functional groups [48]. The main focus of their study was to probe the phosphoproteome of goat MFGM that will be covered in the next section.

#### **3.4. Post-translational modifications (PTM)**

PTMs are chemical modifications that play a key role in functional proteomics. The characterization of PTMs, although challenging, is very important as they regulate protein function and control numerous important biological processes. A large number of different PTMs have been reported, but by far, phosphorylation and glycosylation are the most important and well-studied [54]. Since phosphorylation has a low stoichiometry, there is a need for enrichment of phosphopeptides in an analysis of a complex mixture. The current method to enrich phosphopeptides is based on affinity purification using phosphospecific antibodies immobilized-metal affinity chromatography (IMAC). Zhong et al. optimized the selective isolation of mono- and multi-phosphorylated peptides by using different forms of iron ions [55]. In this study, they selected α-casein and two synthesized mono- and di-phosphopeptides as a model system to demonstrate that NiZnFe<sup>2</sup> O4 was highly selective for multi-phosphopeptides whereas Fe3 O4 , NiFe2 O4 , and ZnFe<sup>2</sup> O4 had a higher affinity for mono-phosphopeptides. Along with the improvements of IMAC for phosphoproteomic experiments, instrument enhancements including improved acquisition speed allowed the identification of many more phosphopeptides per analysis. As mentioned before, in a comprehensive analysis of goat milk MFGM phosphoproteome, Henry et al. used TiO2 for enrichment of the MFGM samples. Using nLC-MS/MS and high resolution mass spectrometer, they characterized the phosphorylation of several key mammary gland proteins in goat MFGM. This group, leveraging the strengths of high resolution and faster acquisition time, reported the detection of 271 sites of phosphorylation on 124 unique goat MFGM proteins [48].

bovine milk proteome, only a limited understanding of the goat innate immune response to mastitis pathogens or the subsequent changes in goat milk protein expression over the course of a clinical infection exists. Nonetheless, like other ruminants that are managed for milk pro-

Proteomic Analysis of Goat Milk

153

http://dx.doi.org/10.5772/intechopen.70082

Soluble mediators of inflammation in bovine milk and plasma during clinical mastitis have been studied extensively using antibody-based strategies [59]. Although antibody-based methodologies are both quantitative and accurate, they have limited detection capabilities. Conversely, mass spectrometric-based proteomic technologies allow for the simultaneous analysis of a larger number of proteins without the reliance on antibodies. Using MS-based proteomics, a number of biomarkers including APPs were identified in bovine serum and milk, which were correlated with pain and disease status [19, 60]. The concentration of most APPs typically increased during infection or inflammation, and the increased levels were relatively stable and persisted for a number of days, or even weeks, after the original insult or stimulus [19]. Despite the fact that our knowledge of the modulation of the bovine milk proteome during mastitis continues to expand, very little comparative data exists on lactat-

In regards to the study of the goat milk proteome, our group detected increases in haptoglobin (Hp), serum amyloid A (SAA), and lactoferrin in the milk of goats following an intramammary infusion of lipopolysaccharide (LPS) to induce coliform mastitis [28]. Other studies also documented significantly increased blood levels of Hp and SAA in an experimentally induced subacute ruminal acidosis in goats [61]. The majority of APPs are known to be glycosylated. Due to the high extent of its carbohydrate moiety, the APP alpha-1-acid glycoprotein (AGP) has been established as a biomarker of inflammation in goats [62]. AGP was also reported to potentially inhibit neutrophil migration to the site of infection, leading to inadequate bacterial clearance and resulting in increased risk of mortality [63]. Further, Heller et al. determined the species-specific reference intervals for four APPs including Hp, SAA, AGP, and lipopolysaccharide-binding protein (LBP), which is a soluble polypeptide that binds to bacterial LPS and increases its proinflammatory activity up to 1000 fold, in goat

Modulations in the expression of goat milk proteins have been examined following an experimental induction of endotoxin mastitis by intra-mammary infusion with LPS. For details of challenge study and sample preparation, see materials and methods section in Ref. [28]. Crude milk samples were separated by 2DE prior to nLC-MS/MS analysis. The unique proteins identified following the 2DE analysis of skim milk from healthy goats and skim milk collected from the same goats 18 h post infusion with LPS are summarized in **Table 2**. In the absence of goat specific database, we used the Swiss-Prot other mammalia taxonomy, which includes only a limited number of goat sequences. Though some goat specific proteins were identified, the majority of the protein identifications were assigned to other species. As shown in this table, caseins constitute the most abundant proteins in milk; thus, a marked number of casein variants, specifically β- and αS2-caseins, which were detected in 13 and 6 separate

**4.1. Effects of experimentally-induced mastitis on the goat proteome**

duction, goats are also susceptible to and affected by mastitis.

ing dairy goats.

milk [64, 65].

The identification of PTMs is especially useful for the detection and characterization of acute phase proteins (APPs) during disease because APPs are subjected to modification. The N-glycan profiles of goat milk lactoferrin were compared with human and bovine milk using advanced mass spectrometry techniques [47]. The characterization of glycan composition established high mannose, hybrid, and complex N-glycans. Among the N-glycan compositions, 37% were sialylated and 34% were fucosylated. This group highlighted the existence of similar glycan composition between human and goat milk and discovered a novel glycan in goat milk that was not detected in human milk. A recent 2016 study investigated N-glycoproteome analysis of MFGMs from a number of mammals' milk [46]. They observed different glycosylation patterns in certain proteins that were previously reported with varying molecular weights based on the analysis by SDS-PAGE. They concluded that these discrepancies were the result of the differences in carbohydrate content of these proteins.


*3.3.2* �*86 PC*

The required image processing software, i.e., OpenCV and TensorFlow, is installed and used for processing the captured images. Here the language used is Python. Similarly the Arduino IDE is used to program the Arduino board. Arduino board is used for the movement of wheels and robotic arm which is shown in **Figure 10**.

#### *3.3.3 Robotic base*

The robotic base consists of an acrylic sheet which has two pair of wheels whose movement is done via a DC geared motor. The base also has a robotic arm.

#### *3.3.4 Robotic arm*

The arm consists of a gripper which is connected to an actuator that moves up and down. There is another actuator connected to the previous one which moves back and forth; this movement is done using DC geared motors. The below given is the flow diagram which shows the work flow of the system.

• On the software side, we have made some major improvements like we have

• Since the robot moves by itself, the number of hardware and software used is reduced which means there is no need for Zigbee and the joystick application

• When it comes to recognizing objects, the Recyclebot only recognizes a small

• But our robot could almost recognize more than 5000 images which prove to

• Also the speed of processing is improved. The accuracy of our robot is far

Skin diseases are becoming the most common health issues worldwide. In this paper we propose a method that detects four types of skin disease using computer vision [10, 11]. The proposed approach involves convolutional neural networks with specific focus on skin disease. The convolutional neural network used in this paper has utilized around 11 layers, namely, convolution layer, pooling layer, activation layer, fully connected layer, and softmax classifier. Images from the DermNet database are used for validating the architecture. The database comprises all types of skin diseases out of which we have considered four different types of skin diseases like acne, keratosis, eczema herpeticum, and urticaria with each class containing around 30–60 different samples. The challenges in automating the process includes the variation of skin tones, location of the disease, specifications of the

• Thus this shows that our robot is much better than its predecessor.

*3.3.5 Skin cancer classification using convolutional neural network*

set of images which is around 1000 to 2000.

be a larger image set than the previous one.

better for an automated robot.

image acquisition system, etc.

**41**

used OpenCV and TensorFlow for processing the obtained image.

*Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial…*

• Also we have used an algorithm called YOLO for object detection and classification which provides better accuracy when compared with the

previous method.

*DOI: http://dx.doi.org/10.5772/intechopen.92091*

anymore.

Classification of object is done using image processing and TensorFlow. Camera is used to capture the image of the object present on the surface which is then matched with the pre-trained dataset. Here the object detection is achieved using YOLO object detection, and the detected object is directed to its respective class. There are several modes that are made available such as plastic, glass, metal, degradable, and nondegradable. Based on the information of the selected mode, either the object is picked up or ignored. The object is picked up using a robotic arm which is controlled using the pre-programmed Arduino board. Once the desired object is picked up in accordance with the selected mode, object classification is completed successfully. Advances are done in proposed method in comparison with the existing method:

*Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial… DOI: http://dx.doi.org/10.5772/intechopen.92091*

#### *3.3.5 Skin cancer classification using convolutional neural network*

Skin diseases are becoming the most common health issues worldwide. In this paper we propose a method that detects four types of skin disease using computer vision [10, 11]. The proposed approach involves convolutional neural networks with specific focus on skin disease. The convolutional neural network used in this paper has utilized around 11 layers, namely, convolution layer, pooling layer, activation layer, fully connected layer, and softmax classifier. Images from the DermNet database are used for validating the architecture. The database comprises all types of skin diseases out of which we have considered four different types of skin diseases like acne, keratosis, eczema herpeticum, and urticaria with each class containing around 30–60 different samples. The challenges in automating the process includes the variation of skin tones, location of the disease, specifications of the image acquisition system, etc.

Classification of object is done using image processing and TensorFlow. Camera

• Some areas in the design of the robot in existing method have limitations; these

• Thus we have made some improvements in the design of the robot by making it a bit smaller, adding a webcam which is weightless and making the wheel

• We have also removed the bins available on top of the robot as the previous one only classifies between two classes but our robot classifies almost between

• The above robot mentioned in the existing method uses manual pickup of the classified waste, but we have added a robotic arm gripper which automatically

• Also the Recyclebot is controlled manually over a Zigbee powered joystick application, but our robot is fully automated and uses Arduino to achieve it.

limitations may be overcome by improving upon the design.

is used to capture the image of the object present on the surface which is then matched with the pre-trained dataset. Here the object detection is achieved using YOLO object detection, and the detected object is directed to its respective class. There are several modes that are made available such as plastic, glass, metal, degradable, and nondegradable. Based on the information of the selected mode, either the object is picked up or ignored. The object is picked up using a robotic arm which is controlled using the pre-programmed Arduino board. Once the desired object is picked up in accordance with the selected mode, object classification is completed successfully. Advances are done in proposed method in comparison with

the existing method:

**40**

**Figure 10.**

*Flow diagram of the system.*

*Dynamic Data Assimilation - Beating the Uncertainties*

base strong and stable.

more than three classes.

picks up the classified object.

*Dynamic Data Assimilation - Beating the Uncertainties*

**References**

[1] Sainath TN, Mohamed AR, Kingsbury B, Ramabhadran B. Deep convolutional neural networks for LVCSR. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE; 2013. pp. 8614-8618. Available from: http://www.cs.toronto.edu/ asamir/papers/icassp13\_cnn.pdf

*DOI: http://dx.doi.org/10.5772/intechopen.92091*

[9] Anand R, Shanthi T, Nithish MS, Lakshman S. Face recognition and classification using GoogleNET architecture. In: Soft Computing for Problem Solving. Singapore: Springer;

[10] Sabeenian RS, Paramasivam ME, Anand R, Dinesh PM. Palm-leaf manuscript character recognition and classification using convolutional neural networks. In: Computing and Network Sustainability. Singapore: Springer;

[11] Shanthi T, Sabeenian RS, Anand R. Automatic diagnosis of skin diseases using convolution neural network. Microprocessors and Microsystems.

2020. pp. 261-269

*Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial…*

2019. pp. 397-404

2020;**76**:103074

[2] Simard PY, Steinkraus D, Platt JC. Best practices for convolutional neural networks. In: International Conference on Document Analysis and Recognition

[3] Lawrence S, Giles CL, Tsoi AC, Back AD. Face recognition: A convolutional neural-network approach. IEEE

Transactions on Neural Networks. 1997;

[4] Liu L, Shen C, van den Hengel A. The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

(ICDAR). 2003. p. 958

**8**(1):98-113

pp. 4749-4757

pp. 1097-1105

(1-3):67-80

**43**

[5] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems. 2012.

[6] Graham B. Fractional max-pooling. 2014. arXiv preprint arXiv:1412.607

[7] Anand R, Shanthi T, Sabeenian RS, Veni S. Real time noisy dataset implementation of optical character identification using CNN. International Journal of Intelligent Enterprise. 2020;**7**

[8] Anand R, Kalkeseetharaman PK, Naveen Kumar S. Automatic facial expressions and identification of different face reactions using convolutional neural network


**1. Introduction**

Arsenic trioxide (ATO) is the main active ingredient of traditional Chinese medicine (TCM) Arsenic. In the 1970s, it was first applied to acute promyelocytic leukemia (APL) with significant efficacy [1] and was approved by the National Medical Products Administration (NMPA) and Food and Drug Administration (FDA) as a first-line treatment for APL in 1999 and 2000, respectively [2,3]. ATO can induce cell di fferentiation, inhibit apoptosis, and exert anti-tumor e ffect [4]. In recent years, research studies have confirmed the significant growth inhibition and apoptosis induction e ffect of ATO in solid tumors, such as liver cancer, breast cancer, stomach cancer, glioma and lung cancer [5–11]. At present, ATO injection has been employed clinically in the treatment of APL and advanced primary liver cancer. However, the unique physicochemical properties of ATO allow it to be rapidly cleared from blood, and it requires daily administration during clinical treatment. At the same time, the uptake of the reticuloendothelial system (RES) makes only a slight amount of ATO reach the tumor site. Nevertheless, considering the potent toxicity of ATO, increasing the dose of ATO will increase the systemic toxicity and cause damage to the liver, kidney, heart, and peripheral nerve [12–14].

Based on the size advantage, nanoparticles (NPs) can exude through the tumor vasculature and effectively delivery the drugs to cells through enhanced permeability and retention (EPR) e ffects [15]. Therefore, it is considered to be a kind of formulation with low toxicity and high stability. Di fferent ATO delivery systems (DDS) have been developed, including magnetic nanoparticles [16], chitosan nanoparticles [17], microspheres [18], liposomes [19], and mesoporous silica nanoparticles [10,20]. These formulations can achieve sustained release of ATO, which can reduce the transient plasma concentration and toxicity of drugs to a certain extent. However, they are still deficient in biocompatibility, and the safety of these systems needs to be verified [21].

Sodium alginate (SA) is a sort of polyanionic polysaccharide alginic acid salt found in brown algae that is water-soluble and has the advantages of anti-tumor e ffect, immune regulation, non-toxic, biodegradability, and excellent biocompatibility [22,23]. It has been approved by FDA for the pharmaceutical industry as an excipient [24,25]. For the past few years, NPs prepared from SA as drug carrier systems have also attracted more attention [26,27]. Red blood cells membrane (RBCM) will be formed into vesicles (RVs) using extrusion or sonication methods [28]. As a drug carrier, it can be attached to the surface of NPs to sustain the release of drugs, avoid elimination by the immune system, increase drug stability, improve biocompatibility and thus prolong drug circulation in vivo [29,30].

Moreover, RBCM coating nanotechnology already has excellent precedents. Che-Ming J et.al [31] demonstrated the synthesis of an RBCM coated polymeric nanoparticle for long-circulating cargo delivery, Jinghan Su et al. [32–34] extensively studied the e ffectiveness of RBCM-camouflaged NPs for treating metastatic breast cancer. In addition, if the non-toxic SA nanoparticles can be encapsulated by natural RBCM and combine the superiorities of sustained release and prolonging residence time, the nano-system can achieve the purpose of maintaining e fficacy and reducing toxicity. In addition, this can provide a new possibility for safe application of ATO.

In this study, RBCM-camouflaged ATO-loaded sodium alginate nanoparticles (RBCM-SA-ATO-NPs, RSANs) were prepared as shown in Figure 1. ATO-loaded sodium alginate nanoparticles (SA-ATO-NPs, SANs) were prepared by the ion crosslinking method, followed by coating of RBCM to obtain RSANs. It was then systematically characterized and evaluated for its efficacy and toxicity. The results indicated that the system might become a promising delivery system for the safe, e ffective, and sustained release of ATO.

**Figure 1.** Preparation and Characterization of RSANs.

### **2. Materials and Methods**


**5. Conclusion**

The aeronautical project quality is essential for providing passengers with acceptable environmental conditions in order to achieve their thermal comfort. The research demonstrates the utmost importance of the aeronautical community (industries, research labs, and universities) in the fluids modeling field and the practical applications in the industry's daily job.

This research results present models based on Computational Fluid Dynamics (CFD) with parameters analysis of temperature and airspeed distributed in a real environment with a cabin sometimes empty, sometimes with passengers standing and/or seated.

It was verified that the temperature and airspeed are influenced by passengers' behavior, whether they are seated or standing in the plane's cabin. Breathing influences the airflow, being able to cause contamination in its environment. So, ventilation from the roof of the cabin promotes more particle dispersion throughout its area.

Another significant aspect to highlight is the importance of achieving the plane's thermal comfort in a cruise. Having an adequate air conditioning system that contributes to the well-being, health, and the aircraft's users' health, as well as the other aspects that influence the passengers' environmental comfort, when based on the cabins' geometry, the armchairs positioning and ergonomics will also contribute

positively in the aircraft's mechanic system. We suggest the use of the air insufflation systems to avoid contamination through the air in the interior of the cabin.

As this research's results demonstrate, simulations in CFD showed that there are three variables, and they must be considered to better evaluate the thermal comfort and indoor air quality in an aircraft cabin, which are- the profile of different temperatures, the airspeed, and human breathing. Therefore, the airflow circulation from the upper (ceiling) to the lower air supply duct (floor) promotes a wider dispersion of particles throughout the cabin that is associated with the characteristics of the mixing of this ventilation through passengers and the airplane's seats. However, it is also important to investigate the airspeed and temperature associated with air humidity.

The use of personalized ventilation is important during the pandemic period of COVID-19, for instance, when the gasper is opened, the airflow must pass between passengers, the air circulation must be oriented directed to the floor's lower duct. Another precaution that helps to protect the passengers is the use of masks and facial protectors during the flights, which must have non-ventilation and be approved by government agencies. Another security measure adopted to protect the people is to remain at a distance from others and always use alcohol gel.

The concern with passenger air transport relates to public health and safety aspects which must be analyzed in order to have a faster response for such risks. This is fundamental so that measures, such as prevention and vaccine development, in addition to efforts to mitigate COVID-19 transmission and dissemination, can be effective. Technical and scientific researches, as well as public policies to aid and enforce these measures must be constant and applied to the aeronautics industry everywhere in the world, with decisions made according to international health regulations. An additional investigation must be carried on about contamination of material carried in the aircraft.

#### **Acknowledgements**

English version by: Samantha A.L. Takatui, Mayor Edson da Silva and Eng. Nilson Carneiro, Prefeitura Municipal de Araraquara, São Paulo, Universidade Federal de São Carlos, PPGEU, Instituto de Pesquisa Tecnológica do Estado de São Paulo, IPT e Universidade de São Paulo, São Carlos.

### **Funding**

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

*Aeronautics - New Advances*


**3. Materials**

The monthly rainfall (P) data of 8 rainfall observation stations and the monthly runoff (Q) data (long data from January 1984 to December 2015) at the hydrological station were collected from the Guizhou Provincial Hydrology and Water Resources Bureau (http://www.gzswj.gov.cn/hydrology\_gz\_new/index.phtml) (accessed on 15 September 2016). The monthly evaporation (E) data (January 1984 to December 2015), which were also derived from the Guizhou Provincial Hydrology and Water Resources Bureau, were measured by the evaporation dish of the hydrological observation station and represented the water evaporation of the water surface or soil. The monthly temperature (T) data

of the weather station corresponding to the runoff time series were obtained from the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/) (accessed on 5 August 2021). The average annual rainfall data for the watershed were interpolated by the Kriging method with the annual rainfall data of all 8 rainfall stations (both inside and outside the watershed). The DEM data with a spatial resolution of 30 m were obtained from the International Scientific and Technical Data Mirror Site, Computer Network Information Center, Chinese Academy of Sciences, which could be downloaded from the Geospatial Data Cloud (http://www.gscloud.cn) (accessed on 12 September 2021). The lithology data were derived from the Karst Scientific Data Center (http://www.karstdata.cn/) (accessed on 12 September 2021), Institute of Geochemistry, Chinese Academy of Sciences.

#### **4. Methodology**

A series of wavelet analysis methods were used to identify the multi-scale impact of climate factors on runoff change in the Yinjiang River watershed. These wavelet analysis methods included MRA [47], CWT [19–21,48,49], XWT [19–21], cross wavelet phase angle (CWPA) [50] and WTC [19]. MRA was carried out using a free MATLAB software package provided by the WaveLab Development Team and available at http://statweb.stanford. edu/~wavelab/, (accessed on 12 September 2021). Other methods, including CWT, XWT, WTC, and CWPA, were carried out using a free MATLAB software package (Mathworks, Natick, MA, USA) kindly provided by Grinsted et al. [19] at http://noc.ac.uk/usingscience/crosswavelet-wavelet-coherence, (accessed on 12 September 2021). The package includes code originally written by Torrence and Compo [20] of the University of Alaska, available at http://paos.colorado.edu/research/wavelets/, (accessed on 12 September 2021). The flowchart for identifying the multi-scale influences of climate factors on runoff changes is shown in Figure 2.

**Figure 2.** Flowchart for identifying the multi-scale influences of climate factors on runoff changes.

#### *4.1. MRA*

Choosing the particular values of a0 = 2 and s0 = 1 corresponds to the dyadic case used in MRA. The aim is to reduce/increase the resolution by a factor of 2 between two scales. Therefore, the approximation of a signal *x*(*t*) at a resolution *j*, denoted by *Aj x*, and the detail of the same function at a resolution *j*, denoted by *Dj x*, are defined by:

$$A\_{\mathcal{X}}^{\dot{j}}(t) = \sum\_{k=-\infty}^{+\infty} C\_{j,k} \psi\_{j,k}(t) \tag{1}$$

$$D\_x^j(t) = \sum\_{k=-\infty}^{+\infty} D\_{j,k} \Phi\_{j,k}(t) \tag{2}$$

where Φ*j*,*k*(*t*) is a scaled and translated basis function called the scaling function [47], which is determined with *ψj*,*k*(*t*) when a wavelet is selected. *Cj*,*k* is the scaling coefficient given the discrete sampled values of *x*(*t*) at resolution *j* and location *k*. It is calculated from Φ*j*,*k*(*t*) in a similar way for the wavelet coefficient *Dj*,*k* from *ψj*,*k*(*t*) for detailed mathematical expressions [49].

The signal *x*(*t*) can be reconstructed from the approximation and detail components as:

$$\mathbf{x}(t) = A\_x^j(t) + \sum\_{j=1}^{J} D\_x^j \tag{3}$$

where *J* is the highest resolution level considered. Since the MRA ensures variance is well captured in a limited number of resolution levels, the analysis of energy distribution in the sampling time series across scales gives a good idea of the energy distribution across frequencies.


**2.3 Fe2O3-Al2O3 system**

Al2O3 does not directly react with Fe2O3, but Al2O3 may react with wustite (FeO) produced during roasting process to form FeO·Al2O3. No pure ferrous oxide (FeO) exists in the actual process. The ratio of oxygen atoms to iron atoms is more than one in wustite, which is generally expressed as FexO(x=0.83~0.95), whose crystal structure is absence type crystallology. For convenience, FeO is expressed as wustite in this thesis. Al2O3 may react with wustite(FeO) to form FeO· Al2O3 in the roasting process. The relationship of *GT* and temperature is shown in figure 2, and the chemical reaction of the equation is as followed:

$$\text{Al}\_2\text{O}\_3 + \text{FeO} = \text{FeO} \cdot \text{Al}\_2\text{O}\_3 \cdot \text{Al}\_T^{\theta} = \text{-30172.2} + \text{9.3T} \text{.} \text{J} \cdot \text{mol}^{-1} \cdot \text{843} \cdot \text{1650K} \tag{1}$$

Fig. 3.  Relationships between

3 3

2 2

5 5

5 5

17 17

Table 5. The

Table 4. The

Al2O3, CaO, SiO2 and Fe2O3 During Roasting Processes 829

roasting temperature, the thermodynamic order that one mole Al2O3 reacts with CaO to

3CaO+ Al2O3=3CaO·Al2O3 -9.9 -28.4 298~1808

CaO+ Al2O3=CaO·Al2O3 -15871.5 -18.1 298~1878

**200 400<PHONE_NUMBER> 1200 1400 1600 1800**

T,K

CaO•Al2O3+ Al2O3=CaO•2Al2O3 2543.8 -9.5 298~2023

*GT* of Al2O3-calcium aluminates system(

**2**

**CaO+Al**

**O3**

**2**

**2**

**O3**

**O3**

**=(1/7)12CaO2**

*GT* and temperature in Al2O3-CaO system

**=CaO2**

**3CaO+Al**

**·7Al**

**2**

J/mol

**·2Al**

**2**

**O3**

**2**

**O**

**3**

B, J/K.mol

13939.7 -65.8 298~1800

*G A BT T* , J/mol)

Temperature, K

**O3**

**12/7CaO+Al**

**1/2CaO+Al**

Reactions A, J/mol B, J/K.mol Temperature, K

*G A BT T* , J/mol)

**=(1/2)CaO2**

> **·Al**

**O3**

**2**

**2**

**O3**

**=3CaO·Al**

*GT* and temperature (T) are shown in figure 5.

318.3 -44.5 298~1800

Figure 3 shows that, the *GT* is negative at 843~1650K, reaction can happen and generate FeO·Al2O3; the *GT* rises with the temperature, the higher temperature is, the lower thermodynamic reaction trends.

### **2.4 SiO2-Fe2O3 system**

SiO2 also does not directly react with Fe2O3, but Al2O3 may react with wustite (FeO) to form FeO·SiO2 (FS) and 2FeO·SiO2(F2S). The relationships of *GT* and temperature is shown in figure 4, and the chemical reactions of the equations are shown in table 3.

Table 3. The *GT* of SiO2- Al2O3 system( *G A BT T* , J/mol)

Figure 4 shows that, the *GT* of SiO2- Al2O3 system are above zero at 847~1500K, so all of the reactions can not happen to form ferrous silicates (FS and F2S).

Fig. 4. Relationships of *GT* and temperature in SiO2-Fe2O3 system

#### **2.5 CaO-Al2O3 system**

Al2O3 can react with CaO to form calcium aluminates such as 3CaO·Al2O3(C3A), 12CaO·7Al2O3(C12A7), CaO·Al2O3(CA) and CaO·2Al2O3 (CA2). As regard as the calcium aluminates only C12A7 can be totally soluble in soda solution, C3A and CA dissolve with a slow speed, and the other calcium aluminates such as CA2 are completely insoluble. Equations that Al2O3 reacted with CaO to form C3A, C12A7, CA and CA2 are presented in table 4.

Figure 5 shows that, the *GT* of reactions of Al2O3 with CaO decreases with the rise of temperature; all reactions automatically proceed to generate the corresponding calcium aluminates at normal roasting temperature (1473~1673K, same as follows); At the same

SiO2 also does not directly react with Fe2O3, but Al2O3 may react with wustite (FeO) to form

*GT* is negative at 843~1650K, reaction can happen and generate

*GT* of SiO2- Al2O3 system are above zero at 847~1500K, so all of

*GT* of reactions of Al2O3 with CaO decreases with the rise of

*GT* and temperature is shown in

*GT* rises with the temperature, the higher temperature is, the lower

Reactions A, J/mol B, J/K.mol Temperature, K

*G A BT T* , J/mol)

FeO+SiO2 =FeO·SiO2 26524.6 18.8 847~1413 2FeO+SiO2 =2FeO·SiO2 -13457.3 30.3 847~1493

**800 900 1000 1100 1200 1300 1400 1500 1600**

**T/K**

*GT* and temperature in SiO2-Fe2O3 system

Al2O3 can react with CaO to form calcium aluminates such as 3CaO·Al2O3(C3A), 12CaO·7Al2O3(C12A7), CaO·Al2O3(CA) and CaO·2Al2O3 (CA2). As regard as the calcium aluminates only C12A7 can be totally soluble in soda solution, C3A and CA dissolve with a slow speed, and the other calcium aluminates such as CA2 are completely insoluble. Equations that Al2O3 reacted with CaO to form C3A, C12A7, CA and CA2 are presented in

temperature; all reactions automatically proceed to generate the corresponding calcium aluminates at normal roasting temperature (1473~1673K, same as follows); At the same

Figure 3 shows that, the

**2.4 SiO2-Fe2O3 system**

thermodynamic reaction trends.

Figure 4 shows that, the

Fig. 4. Relationships of

**2.5 CaO-Al2O3 system**

Figure 5 shows that, the

table 4.

FeO·Al2O3; the

Table 3. The

FeO·SiO2 (FS) and 2FeO·SiO2(F2S). The relationships of

figure 4, and the chemical reactions of the equations are shown in table 3.

*GT* of SiO2- Al2O3 system(

the reactions can not happen to form ferrous silicates (FS and F2S).

**FeO + SiO2 = FeO·SiO2**

> **2FeO + SiO2 = 2FeO·SiO2**

△G/(KJ**·**Mol-1

/(KJ·Mol-1)

*T G*

)

Table 4. The *GT* of Al2O3-CaO system( *G A BT T* , J/mol)

The relationships between *GT* and temperature (T) are shown in figure 5.

Fig. 5. Relationships between *GT* and temperature in Al2O3-CaO system

Table 5. The *GT* of Al2O3-calcium aluminates system( *G A BT T* , J/mol)

Thermodynamics of Reactions Among

**-150 -140 -130 -120 -110 -100 -90 -80 -70**

△

*T G*

Fig. 7. Relationships between

**2.7 SiO2- calcium aluminates system**

between

Figure 8 shows that, the

G/(KJ·Mol-1

/(KJ·Mol-1)

)

Al2O3, CaO, SiO2 and Fe2O3 During Roasting Processes 831

**200 400<PHONE_NUMBER> 1200 1400 1600**

**T/K**

Figure7 shows that, SiO2 reacts with CaO to form γ-C2S when temperature below 1100K, but β-C2S comes into being when the temperature above 1100K. At normal roasting temperature,

Figure 5 ~ figure 7 show that, CaO reacts with SiO2 and Al2O3 firstly to form C2S, and then

In the CaO-Al2O3 system, if there exists some SiO2, the newly formed calcium aluminates are likely to react with SiO2 to transform to calcium silicates and Al2O3 because SiO2 is more acidity than that of Al2O3. The reaction equations are presented in table 7, the relationships

the reaction (3CA2+SiO2=C3S+6Al2O3) can not happen when the roasting temperature is above 900K , i.e., the lowest calcium-to-aluminum ratio calcium aluminates cannot transform to the highest calcium-to-silicon ratio (CaO to SiO2 molecular ratio) calcium

is also more than zero; but the other calcium aluminates all can react with SiO2 to generate calcium silicates at 800~1700K. The thermodynamic sequence of calcium aluminates reaction

·

**2**

**(**γ**)**

**SiO2** **(**

**3CaO+SiO2**

ß

**=(1/2)3CaO**

**)**

**=2CaO**

·

*GT* and temperature

**SiO2**

**=CaO**

**(3/2)CaO+SiO**

**CaO+SiO2**

·

**SiO2**

**=CaO**

(wollastonite)

·

**SiO2** (

pseud-wollastonite

·

**2SiO2**

**=3CaO**

·

**SiO2**

*GT* of all the reactions increases with the temperature increases;

*GT* of reaction(3CA+ SiO2=C3S+3Al2O3)

)

**2CaO+SiO2**

**=2CaO**

**2CaO+SiO2**

*GT* and temperature are shown in figure 8.

silicate; when the temperature is above 1500K, the

with SiO2 is firstly C3A, and then C12A7, CA, CA2.

the thermodynamic order of forming calcium silicate is C2S, C3S, C3S2, CS.

C12A7. Therefore, it is less likely to form aluminium silicates in roasting process.

**CaO+SiO2**

When CaO is insufficient, redundant Al2O3 may promote the newly generated high calciumto-aluminum ratio (CaO to Al2O3 mole ratio) calcium aluminates to transform into lower calcium-to-aluminum ratio calcium aluminates. The reactions of the equations are presented in table 5:

The relationships between *GT* of reactions of Al2O3-calcium aluminates system and temperature (T) are shown in figure 6.

Fig. 6. Relationships between *GT* of reactions Al2O3-calcium aluminates system and temperature

Figure 6 shows that, Gibbs free energy of the reaction of Al2O3-calcium aluminates system are negative at 400~1700K, and all the reactions automatically proceed to generate the corresponding low calcium-to-aluminum ratio calcium aluminates; Except for the reaction of Al2O3-C12A7, the *GT* of the rest reactions decreases with the rise of temperature and becomes more negative. Comparing figure 4 with figure 5, it can be found that Al2O3 reacts with CaO easily to generate C12A7.


**5. References**

	- [3] Federal Communication Commission (2002). Revision of Part 15 of the communication's rules regarding ultra wideband transmission systems. First report and order, ET Docket 98-153, FCC 02-48

**Power Allocation Procedure for Wireless Sensor**

**Communications and Radar Capabilities**

Gholamreza Alirezaei, Rudolf Mathar and Daniel Bielefeld

Additional information is available at the end of the chapter

cited.

http://dx.doi.org/10.5772/53005

**1. Introduction**

**Networks with Integrated Ultra-Wide Bandwidth**

**Chapter 8**

In this chapter, we analyze the problem of power allocation for a distributed wireless sensor network with sensor nodes based entirely on ultra-wide bandwidth (UWB) technology. The network is used to perform object detection as well as object classification, where the absence, the presence, or the type of an object is observed by the sensors independently. UWB signals can be used for data communication between the sensor nodes as well as for radar applications. The approach of misemploying the communication sensors as radar sensors, such that the data transmission is misused as a radar beam in order to detect or to classify a target object, helps in realizing an energy-efficient radar system with compact and cheap sensor nodes. A further advantage of such radar systems is the fulfillment of major requirements of wireless sensor networks. This exploitation presupposes that the integration of sensing functionality into usual UWB sensors is implementable easily without the usage of any additional hardware units. Since the compact and low complexity UWB sensors are limited in power and communication capabilities, the detection and classification performance of a single sensor is restricted compared to that of a common complex radar system. To obtain an appropriate overall system performance, we consider the case of distributed detection and classification, where the local observations of the sensors are fused into a reliable global decision. Due to noisy communication channels and differences in distances between the object and the sensor nodes, both, the observations and their transmissions are unequally interfered. One simple way to suppress noise interference is to increase the power of each sensor node. But if the total power of the entire network is limited, then power allocation procedures are needed in order to increase the overall detection and classification probabilities. In general, it is challenging to evaluate the detection and the classification probabilities analytically, if possible at all. This particularly holds for the detection probability under a Neyman-Pearson-hypotheses-test criterion as well as for the classification probability under a Bayesian-hypotheses-test criterion [5]. This limits the usability of these criterions for analytical optimization of power allocation. Bounds, such as the Bhattacharyya bound [8], are also difficult to use for optimizing multidimensional

> ©2013 Alirezaei et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly

©2013 Alirezaei et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**2.5. Algae**

The water impacts of algal propagation vary widely by technology and environmental conditions, with water use ranging from minimal up to 3,650 L L−1 of biodiesel or advanced biofuel produced [33]. Freshwater is needed to replace water losses from open ponds, even when halophilic organisms are used. While the volumes in photobioreactors are relatively small, cooling requirements, usually met by freshwater, are large. Water impacts of conversion technologies result from competition from often scarce freshwater supplies.

> time period might be several centuries or millenia, depending on climate and vegetation. Because of this long time factor, soils are considered to be non-renewable. They are heterogeneous and highly diverse components of ecosystems that form over long time periods under the influence of parent mineral material, climate, landscape position and biological activity. As the base of bioenergy production systems, soils constitute a major factor that interacts with water to determine the type and amount of plant biomass production (**Table 1**, [2]). They provide the physical anchor which tie plants to the earth, supply water and mineral nutrients for plant growth, decompose and recycle organic material and residues and mediate hydrological processes [39–41]. Bioenergy feedstock systems are part of agricultural and forest management systems that provide multiple ecosystem products and services [42]. These include plant biomass, water flow, water quality control, biodiversity, cultural heritage, and carbon storage. Soils are important factors in each of these services. Therefore, it is critical that in the process of managing soils for bioenergy production, soils must be managed to sustainably provide a wide range of ecosystem services important to human communities. Maintaining

**Black** = direct physical effect, **Green** = effect mediated through the crop specific attributes such as root or canopy structure, **Blue** = effect is both physical and plant -mediated

**Soil Texture Soil Organic**

**Water Supply Availability S S S Soil Moisture S S W S Evaporative Losses M S W W**

**Groundwater Recharge S S S**

**Table 1.** Interdependencies of water and soil resources (adapted from [2]).

Key: **S** = soil effect on water W = water effect on soil M = mutual effect

**Matter**

**Water Runoff M S S S W Precipitation Interception W**

**Surface Water Turbidity S S S Eutrophication S S S**

**Mineral Nutrient Availability**

Impacts of Bio-Based Energy Generation Fuels on Water and Soil Resources

**Water Holding Capacity**

http://dx.doi.org/10.5772/intechopen.74343

**Erosion**

119

The focus on renewable energy sources has raised concerns about environmental effects. In particular, the increase in the use of woody biomass, agricultural crops, agricultural residues and processing wastes residues as feedstocks for bioenergy production has intensified questions about potential impacts on water quality and soil sustainability. Intensification of

the quality of soils will ultimately ensure maintenance of water quality.

**4. Best management practices**

#### **2.6. Electrical generation impacts**

In general, water impacts of biomass powered electricity generation remain similar to fossil fuel pathways, with large water withdrawals but low consumptive use ranging from 0–1800 L MWh−1 [34]. Cooling water, which may contain some salts, is returned at higher temperature to the source stream or basin, with variable ecological impact. Water requirements for biofuel processing continue to improve. Use per tonne of feedstock has decreased dramatically for both corn and sugarcane ethanol. For instance, the consumptive water use of ethanol-sugar mills in Southeast Brazil has decreased from 15 m3 Mg−1 of sugarcane bagasse prior to 2008 to <3 m3 Mg−1 in 2008 [35]. However, in water stressed regions new or expanded facilities may still not be approved due to the associated water demand [35]. While, untreated effluent can create water quality problems, process water offers an opportunity to recover and recycle nutrients. Biofuel facilities with zero liquid discharge have been operating in the U.S. since 2006 and continue to expand worldwide. Technological improvements in water recovery and recycling have progressed to the point that some facilities are able to use municipal wastewater and some have achieved closed loop recycling of process water.