**3. Drought impacts on chemical composition and conversion**

#### **3.1 Biomass chemical composition**

The biomass supply risks related to drought are substantial and unfortunately extend to biomass quality as well as overall yields as discussed above. Crop yields are often reduced during drought conditions as plants do not have the water needed for basic functions like maintaining cell turgor pressure and performing photosynthesis [26]. The impact of drought conditions on yield as well as plant biochemical functions is complex and different plant types, species, and genotypes may vary in their tolerance and responses to drought [27, 28]. Species like *Miscanthus* are reported to be more sensitive to water deficiencies [29] while crops like sorghum [16], reed canary grass [27], and switchgrass [27, 30] display some level of drought tolerance. In addition, plants use different survival strategies to deal with environmental stressors; for example, there is less carbohydrate hydrolysis in cool-season forbs than in cool-season grasses during osmotic stress that occurs when plants

experience drought requiring plant cell walls to compensate for the external changes in water with solute concentrations [31]. Soluble sugar synthesis in plants has been shown to occur in response to water stress in order to increase osmotic potential with significant accumulation of soluble sugars measured in switchgrass exposed to drought conditions [32].

Complicating reductions in crop yields, plants experience compositional changes during drought; increased extractive components, including soluble sugars, and decreased structural sugars were reported for important potential bioenergy crops like switchgrass, *Miscanthus*, mixed grasses, and corn stover [26, 32–34]. Studies have even observed reduced lignification in some cases possibly resulting from decreased plant growth as well as changes in lignin component distribution in plant cells impacting cell wall degradability [33–35]. These compositional changes can greatly impact yield of bioenergy conversion products from these biomass resources. It should also be noted that if these decreases in lignocellulosic components are compounded with decreases in dry biomass yield the estimated product yield can be even further reduced in drought-stressed crops [33].

To demonstrate the effect of large-scale drought on plant composition, data collected through the Regional Feedstock Partnership (RFP) was analyzed [36]. The RFP completed long-term field trials beginning in 2008 for potential bioenergy crops grown across the U.S. unintentionally providing a unique snapshot of how drought could impact the bioenergy industry when nationwide drought covered 65% of the continental U.S. in 2012 during the field trials [36–38]. Four RFP crops—*Miscanthus*, mixed grasses, switchgrass, and energycane—were selected to examine the impact of drought on plant chemistry. Each crop field site, according the U.S. Drought Monitor [37], experienced a year with drought conditions and a control year with minimal to no drought (**Figure 3**). *Miscanthus*, mixed grasses, and switchgrass data were from 2010, non-drought control year, and 2012, a year with significant drought. *Miscanthus* was located in Saunders County, NE; switchgrass in Day County, SD; and mixed grasses in Ellis County, KS each grown under three nitrogen application levels with three to four replicates. In Tift County, GA, where the energycane field site was located, a drought occurred in 2011 as opposed to 2012, and the non-drought control year used was 2009 as shown in the insets in **Figure 3**. Five genotypes were each grown on three replicate plots for the energycane field site.

Across four crop types, multiple energycane genotypes, and a variety of nitrogen fertilizer treatments, it is clear that biomass from drought years had lower lignocellulosic components than non-drought years, depicted by the differences in glucan, xylan, and lignin greater than zero (**Figure 4a–c**). Glucan was as much as 10% lower for biomass produced during a drought year (**Figure 4a**), while lignin was up to 5.5% lower (**Figure 4c**) and xylan up to 3.5% lower (**Figure 4b**). These differences are hypothesized to result from less lignification during reduced plant growth and increased synthesis of soluble components that support osmoregulation, in favor of synthesis of lignocellulosic components as hypothesized in previous studies [17, 33, 39]. It should be noted that not all research plots included in the analysis had greater lignocellulosic components in a non-drought year (differences less than zero shown by dotted lines in **Figure 4**), which is probably a result of the complex agronomic and environmental factors that can simultaneously impact plant yield and composition. Previously reported results on similar RFP samples indicated that along with the year-to-year variability, including drought and non-drought years, agronomic factors of nitrogen treatment and genotype also significantly impacted biomass yields and sustainability measurements [36]. Future studies are necessary to examine the complexity of the combination of these factors using multivariate analysis techniques that include, but are not limited to, drought. In addition, compositional changes in response to drought in the

**51**

**Figure 4.**

**Figure 3.**

also likely play a role.

**3.2 Biochemical conversion processes**

*Drought Impacts on Bioenergy Supply System Risk and Biomass Composition*

literature are mixed. A number of studies report hemicellulose and lignin contents decreasing after drought treatments [33–35, 40], and in contrast other studies report hemicellulose and lignin contents remaining unchanged or increasing under drought conditions [33, 39, 41]. These differences are not completely understood; however, studies have suggested they arise from differences in drought severity and timing [33, 34, 39], genetics [35], and species specific differences [31, 42]. In addition, other environmental parameters like soil nutrient content and texture, timing of precipitation, growing degree days, and optimal growing temperatures

*Difference between percent glucan (a), xylan (b), and lignin (c) content for four crops grown in a year with minimal to no drought and a year with drought conditions shown in Figure 3. Dotted lines represent no difference. Energycane—n = 15, Miscanthus—n = 12, mixed grasses—n = 9, and switchgrass—n = 11.*

*U.S. Drought Monitor maps displaying continental U.S. drought conditions prior to the time of harvest in October 2010, a year with minimal drought, and 2012, a year with drought conditions covering much of the U.S. The inset displays Georgia drought conditions for October 2009 and 2011. Blue shapes indicate the approximate location of each biomass crop: triangles ( ) for Miscanthus, circles ( ) for mixed grasses, diamonds*

Drought-induced alterations to plant composition can significantly impact the yield of conversion products. Changes in biomass composition were exhibited by

*DOI: http://dx.doi.org/10.5772/intechopen.85295*

*( ) for switchgrass, and stars ( ) for energycane.*

*Drought Impacts on Bioenergy Supply System Risk and Biomass Composition DOI: http://dx.doi.org/10.5772/intechopen.85295*

#### **Figure 3.**

*Drought - Detection and Solutions*

drought conditions [32].

experience drought requiring plant cell walls to compensate for the external changes in water with solute concentrations [31]. Soluble sugar synthesis in plants has been shown to occur in response to water stress in order to increase osmotic potential with significant accumulation of soluble sugars measured in switchgrass exposed to

Complicating reductions in crop yields, plants experience compositional changes during drought; increased extractive components, including soluble sugars, and decreased structural sugars were reported for important potential bioenergy crops like switchgrass, *Miscanthus*, mixed grasses, and corn stover [26, 32–34]. Studies have even observed reduced lignification in some cases possibly resulting from decreased plant growth as well as changes in lignin component distribution in plant cells impacting cell wall degradability [33–35]. These compositional changes can greatly impact yield of bioenergy conversion products from these biomass resources. It should also be noted that if these decreases in lignocellulosic components are compounded with decreases in dry biomass yield the estimated product

To demonstrate the effect of large-scale drought on plant composition, data collected through the Regional Feedstock Partnership (RFP) was analyzed [36]. The RFP completed long-term field trials beginning in 2008 for potential bioenergy crops grown across the U.S. unintentionally providing a unique snapshot of how drought could impact the bioenergy industry when nationwide drought covered 65% of the continental U.S. in 2012 during the field trials [36–38]. Four RFP crops—*Miscanthus*, mixed grasses, switchgrass, and energycane—were selected to examine the impact of drought on plant chemistry. Each crop field site, according the U.S. Drought Monitor [37], experienced a year with drought conditions and a control year with minimal to no drought (**Figure 3**). *Miscanthus*, mixed grasses, and switchgrass data were from 2010, non-drought control year, and 2012, a year with significant drought. *Miscanthus* was located in Saunders County, NE; switchgrass in Day County, SD; and mixed grasses in Ellis County, KS each grown under three nitrogen application levels with three to four replicates. In Tift County, GA, where the energycane field site was located, a drought occurred in 2011 as opposed to 2012, and the non-drought control year used was 2009 as shown in the insets in **Figure 3**. Five genotypes were each

Across four crop types, multiple energycane genotypes, and a variety of nitrogen fertilizer treatments, it is clear that biomass from drought years had lower lignocellulosic components than non-drought years, depicted by the differences in glucan, xylan, and lignin greater than zero (**Figure 4a–c**). Glucan was as much as 10% lower for biomass produced during a drought year (**Figure 4a**), while lignin was up to 5.5% lower (**Figure 4c**) and xylan up to 3.5% lower (**Figure 4b**). These differences are hypothesized to result from less lignification during reduced plant growth and increased synthesis of soluble components that support osmoregulation, in favor of synthesis of lignocellulosic components as hypothesized in previous studies [17, 33, 39]. It should be noted that not all research plots included in the analysis had greater lignocellulosic components in a non-drought year (differences less than zero shown by dotted lines in **Figure 4**), which is probably a result of the complex agronomic and environmental factors that can simultaneously impact plant yield and composition. Previously reported results on similar RFP samples indicated that along with the year-to-year variability, including drought and non-drought years, agronomic factors of nitrogen treatment and genotype also significantly impacted biomass yields and sustainability measurements [36]. Future studies are necessary to examine the complexity of the combination of these factors using multivariate analysis techniques that include, but are not limited to, drought. In addition, compositional changes in response to drought in the

yield can be even further reduced in drought-stressed crops [33].

grown on three replicate plots for the energycane field site.

**50**

*U.S. Drought Monitor maps displaying continental U.S. drought conditions prior to the time of harvest in October 2010, a year with minimal drought, and 2012, a year with drought conditions covering much of the U.S. The inset displays Georgia drought conditions for October 2009 and 2011. Blue shapes indicate the approximate location of each biomass crop: triangles ( ) for Miscanthus, circles ( ) for mixed grasses, diamonds ( ) for switchgrass, and stars ( ) for energycane.*

#### **Figure 4.**

*Difference between percent glucan (a), xylan (b), and lignin (c) content for four crops grown in a year with minimal to no drought and a year with drought conditions shown in Figure 3. Dotted lines represent no difference. Energycane—n = 15, Miscanthus—n = 12, mixed grasses—n = 9, and switchgrass—n = 11.*

literature are mixed. A number of studies report hemicellulose and lignin contents decreasing after drought treatments [33–35, 40], and in contrast other studies report hemicellulose and lignin contents remaining unchanged or increasing under drought conditions [33, 39, 41]. These differences are not completely understood; however, studies have suggested they arise from differences in drought severity and timing [33, 34, 39], genetics [35], and species specific differences [31, 42]. In addition, other environmental parameters like soil nutrient content and texture, timing of precipitation, growing degree days, and optimal growing temperatures also likely play a role.

#### **3.2 Biochemical conversion processes**

Drought-induced alterations to plant composition can significantly impact the yield of conversion products. Changes in biomass composition were exhibited by

RFP crops in response to drought stress, where the combined reduction of both structural carbohydrates and biomass yield led to an average 10–15% decrease in theoretical ethanol yield per Mg of dry biomass for *Miscanthus*, corn stover, and mixed perennial grasses [33]. In the 2012 drought year, mixed grasses grown in Kansas had only 10% of the dry biomass yield obtained in the non-drought year and *Miscanthus* dry biomass yield in Nebraska was reduced by an average of 14% [38]. These dry biomass decreases coupled with carbohydrate reductions shown in **Figure 4** severely reduce theoretical product yields. Interestingly, energycane in Georgia and switchgrass in South Dakota did not have dramatic decreases in above-ground biomass yield, which may be due to strong responses to other factors like temperature in the case of energycane, and the reported drought tolerance of switchgrass [38]. Theoretical ethanol yield is often used to demonstrate conversion potential for bioenergy crops based on carbohydrate compositions; however, it is just an estimate of potential yield and is based on assumptions of 100% conversion of carbohydrates to ethanol. In reality, there are many other considerations regarding biomass composition that can affect the pretreatment, enzymatic hydrolysis, and fermentation steps that are necessary to convert biomass to products in biochemical conversion. Hoover et al. [34] reported that *Miscanthus* carbohydrate yields from dilute-acid pretreatment and enzymatic hydrolysis were actually higher in drought affected plants compared to those grown in a non-drought year, which was hypothesized to be a result of higher extractable glucose and lower lignin contents. It is thought that reduced lignin content, observed in some drought-stressed plants, can decrease recalcitrance by creating better access to cell wall carbohydrates and increasing conversion efficiency, but changes in lignin distribution in tissues may also play a role in cell wall degradability in water stressed plants [35, 43]. The increase in carbohydrate yields is not isolated to dilute-acid pretreatment and enzymatic hydrolysis, as drought-stressed *Miscanthus* had increased carbohydrate yields in mild-alkali pretreatment and enzymatic saccharification [39] and after a mild hot water pretreatment and saccharification in nutrient rich environments [28]; in both studies this trend was either less pronounced or not present for leaves when compared to stems. A tall fescue mixture also had few significant increases in carbohydrate conversion yields, thought to be a result of less severe drought growing conditions [34]. A recent report documented increased extractability of pectin components in the cell wall ultrastructure of loblolly pine in response to low soil moisture [44]. Increases in cell wall elasticity have been observed under moisture stress conditions in *Pinus radiata* and may be related to drought tolerance [45]. Pattathil et al. [44] suggested that stress-induced alterations in cell wall elasticity may involve cell wall loosening processes that result from rearrangement of structural cell wall components like pectins and hemicelluloses. Increased elasticity of plant cell walls in biomass may pose further challenges to feeding, handling, and physical/mechanical deconstruction of biomass that is requisite for biochemical conversion. Understanding the changes in cell wall structure, chemical components, and physical properties imparted by drought stress is critical to informing how these properties can be exploited to improve bioprocessing of lignocellulosic feedstocks to biofuels and co-products.

It must also be considered how drought impacts the formation of certain degradation products that decrease conversion efficiencies though inhibition of enzymes during enzymatic hydrolysis and microorganisms during the fermentation step in a biochemical conversion process. For *Miscanthus* pretreated with dilute acid, enzymatic and fermentation inhibitors did not increase, however, this was likely a result of the dilute-acid pretreatment temperatures being lower than those required to form inhibitors [34]. In contrast, fermentation inhibitors were increased in drought stressed switchgrass in a study by Ong et al. [32] where the switchgrass was

**53**

*Drought Impacts on Bioenergy Supply System Risk and Biomass Composition*

chemically pretreated using ammonia fiber expansion (AFEX). Specifically, the increase in soluble sugars formed during drought conditions generated pyrazines and imidazoles in the AFEX pretreatment that inhibited growth of the fermentation organism *S. cerevisiae*. These two studies highlight the need to understand drought impacts on plant biochemistry as well as intermediate and final product yields in order to mitigate these impacts. For example, technology developers in areas that are non-irrigated and prone to drought, either currently or in future climate scenarios, should consider the best pretreatment options for a biochemical conversion process based on the chemical profile of drought-stressed feedstock. In addition, a refiner could tailor pretreatment severity to the chemical composition of the biomass entering a facility to optimize product yields by limiting inhibitor formation with the least reduction in carbohydrate product yields. Future research and development activities might focus on enzyme and microorganism development to better handle inhibitors formed as a result of increased extractive components during uncontrollable environmental conditions. Finally, blending either prior to pretreatment or between different steps in the conversion process could be used to control intermediate or product yields and/or reduce concentrations of

Drought is a risk for the bioenergy industry that is likely to increase in future years. Current knowledge and resources regarding drought impacts on crop yields, quality of biomass, and conversion performance can be used for determining research and development directions and mitigation strategies. Weather patterns and water resources are important considerations early in the process of site and feedstock selection for a facility where matching genotypes to conditions can support optimization of yields. Irrigation may be an option in certain cases, but there are implementation costs and water resources may not be an available or sustainable option given that a vast amount of water resources are currently consumed for agriculture. The scenarios in this chapter examine an alternative approach demonstrating that supply system design can reduce supply chain risk related to drought; these advanced supply systems hold promise for future biorefineries. Supply risk associated with drought needs to consider crop yield losses, in addition to biomass chemical changes. Data from a RFP field study of four energy crops, representing a variety of nitrogen application treatments and genotypes, showed how biomass lignocellulosic components—glucan, xylan, and lignin—were lower for a drought year compared to a non-drought year. Current literature was used to describe how drought related chemical changes propagate from the field through the conversion process, and planning and mitigation can be implemented throughout the system to reduce risk to the biomass producer and biorefinery. Drought induced chemical changes can create inhibitors during pretreatment, a step in biochemical conversion processes, that decrease the efficiency of the conversion process, which reinforces the need for careful selection of pretreatment methodology and severity based on location and biomass used. In addition, research and development is necessary for enzyme and microorganism development as well as to fully understand species' specific response to drought and support breeding programs to produce bioenergy cultivars with traits like increased water use efficiency. Finally, an advanced supply system can supply a refinery with more consistent biomass amounts year to year reducing operating risk, but a refinery may still receive feedstock with varying quality, even in a given year. Therefore, in-line techniques to monitor biomass chemistry entering a facility could be used to blend biomass or intermediates to

*DOI: http://dx.doi.org/10.5772/intechopen.85295*

inhibitors to tolerable levels [46].

**4. Conclusions**

*Drought Impacts on Bioenergy Supply System Risk and Biomass Composition DOI: http://dx.doi.org/10.5772/intechopen.85295*

chemically pretreated using ammonia fiber expansion (AFEX). Specifically, the increase in soluble sugars formed during drought conditions generated pyrazines and imidazoles in the AFEX pretreatment that inhibited growth of the fermentation organism *S. cerevisiae*. These two studies highlight the need to understand drought impacts on plant biochemistry as well as intermediate and final product yields in order to mitigate these impacts. For example, technology developers in areas that are non-irrigated and prone to drought, either currently or in future climate scenarios, should consider the best pretreatment options for a biochemical conversion process based on the chemical profile of drought-stressed feedstock. In addition, a refiner could tailor pretreatment severity to the chemical composition of the biomass entering a facility to optimize product yields by limiting inhibitor formation with the least reduction in carbohydrate product yields. Future research and development activities might focus on enzyme and microorganism development to better handle inhibitors formed as a result of increased extractive components during uncontrollable environmental conditions. Finally, blending either prior to pretreatment or between different steps in the conversion process could be used to control intermediate or product yields and/or reduce concentrations of inhibitors to tolerable levels [46].


**3. Results**

The sample of images gathered in the investigation includes forested lands (**Figure 2**), agricultural lands (**Figure 3**), residential environments (**Figure 4**) (known as urban savanna), downtown-like environments (**Figure 5**) (known as cliff detritus), industrial sites (**Figure 6**), and open water (**Figure 7**) [32].

A Visual Quality Prediction Map for Michigan, USA: An Approach to Validate Spatial Content http://dx.doi.org/10.5772/intechopen.79490 119

**Figure 2.** Image of sample number 67 of a forested landscape in the upper peninsula of Michigan (visual score of 38.548).

**Figure 3.** Image of sample number 80 of a farmland landscape in north-east of the lower peninsula of Michigan (visual score of 44.09).

regarded as close to unanimous in their agreement. The W test statistic approximates a Chisquare distribution with n−1 degrees of freedom (Eq. (3)). If computed values for Chi-square (results of (Eq. (3)) are greater than significant values in a Chi-square table for n−1 degrees of freedom (in this case 29 = 30–1), then there is a high level of agreement/concordance—the

**Variable Score** A. Purifies Air +1 0–1 B. Purifies Water +1 0–1 C. Builds Soil Resources +1 0–1 D. Promotes Human Cultural Diversity +1 0–1 E. Preserves Natural Resources +1 0–1 F. Limits Use of Fossil Fuels +1 0–1 G. Minimizes Radioactive Contamination +1 0–1 H. Promotes Biological Diversity +1 0–1 I. Provides Food +1 0–1 J. Ameliorates Wind +1 0–1 K. Prevents Soil Erosion +1 0–1 L. Provides Shade +1 0–1 M. Presents Pleasant Smells +1 0–1 N. Presents Pleasant Sounds +1 0–1 O. Does not Contribute to Global Warming +1 0–1 P. Contributes to the World Economy +1 0–1 Q. Accommodates Recycling +1 0–1 R. Accommodates Multiple Use +1 0–1 S. Accommodates Low Maintenance +1 0–1 T. Visually Pleasing +1 0–1

X2 = m(n − 1)W (3)

The sample of images gathered in the investigation includes forested lands (**Figure 2**), agricultural lands (**Figure 3**), residential environments (**Figure 4**) (known as urban savanna), downtown-like environments (**Figure 5**) (known as cliff detritus), industrial sites (**Figure 6**),

predicted scores and the actual scores are in agreement.

**Table 1.** Variables for the environmental quality/health index in Eq. (1).

**3. Results**

Total score

**Health index**

118 Land Use - Assessing the Past, Envisioning the Future

and open water (**Figure 7**) [32].

**Figure 4.** An image of a residential landscape (urban savanna with visual quality score of 46.587), sample number 12 in the northwest of the lower peninsula.

**Table 2** presents the rankings of the images from the study. The predicted ranks are scores generated from the expected a land-use scores and applying the expected score to a land-use map of the study area. The actual scores are values taken and measured from random sites in the study area. Kendall's Concordance analysis revealed a Chi-square score of 54.267. The

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121

**Property Predicted ranking Mean expected score Actual score Set 2 ranking**

 63.06312 55.94164 15 63.06312 46.58693 8 63.06312 70.73992 20 63.06312 68.34915 18

 81.56982 84.30117 24 81.56982 86.72108 27 81.56982 82.76631 23 81.56982 84.77721 25

 55.92384 57.74200 16 55.92384 45.25820 7 55.92384 44.09459 6 55.92384 53.27078 13

 90.91938 94.63968 28 90.91938 96.94017 29 90.91938 78.77838 22 90.91938 97.67801 30

 42.67716 46.98920 9 42.67716 36.70580 2 42.67716 36.70580 2 42.67716 38.58380 4

 44.99797 47.78978 11 44.99797 35.62803 1 44.99797 47.27917 10 44.99797 41.39820 5

Residential l 18 63.06312 73.69797 21

Downtown 23 81.56982 69.28333 19

Farmland 13 55.92384 59.12320 17

Industrial 28 90.91938 86.56068 26

Forested 3 42.67716 54.40120 14

Water 8 44.99797 52.89468 12

**Table 2.** Comparison of ranks between the average expected score and actual site photographs.

**Figure 5.** An image of sample number 73 of a downtown environment (cliff detritus with a visual score of 82.766).

**Figure 6.** An image of sample number 77 of an industrial environmental (with a visual score of 78.778).

**Figure 7.** An image of sample number 46 of a primarily open water environment of Lake superior (with a visual score of 41.498).

**Table 2** presents the rankings of the images from the study. The predicted ranks are scores generated from the expected a land-use scores and applying the expected score to a land-use map of the study area. The actual scores are values taken and measured from random sites in the study area. Kendall's Concordance analysis revealed a Chi-square score of 54.267. The

**Figure 5.** An image of sample number 73 of a downtown environment (cliff detritus with a visual score of 82.766).

120 Land Use - Assessing the Past, Envisioning the Future

**Figure 6.** An image of sample number 77 of an industrial environmental (with a visual score of 78.778).

**Figure 7.** An image of sample number 46 of a primarily open water environment of Lake superior (with a visual score

of 41.498).

**Table 2.** Comparison of ranks between the average expected score and actual site photographs.

this category. The study revealed numerous landscapes with scores in the 40s and 50s. Such landscapes are often modestly preferred environments. Scores in the 70s are less preferred and scores near or above 100 are not preferred [23]. Across the state, least preferred environments were rarely encountered, primarily in the southeastern portion of the state. The 95% confidence interval for any scores is ±5 points [33]. Thus, it takes a separation of 10 points for

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123

The average expected score (the sum of the products between the number of grid cells by their expected score, divided by the total number of grid cells) for the whole state is approximately 47.4. This score suggests that collectively the whole state of Michigan may not be the most beautiful of all environments; on the other hand, the environmental score is quite respectable

To understand the context of the average expected score in Michigan, it is useful to explore the land-uses, published perceptions, recreational activities, and agriculture within the state. In an article published on the 29th of June, 2015, the Detroit Free Press reported that Thrillist who ranked all of the states in America, placed Michigan as the top state [34]. While the results of the list do not definitively demonstrate that Michigan is at the top, it does indicate that the environment of Michigan merits consideration as a noteworthy place in regard to visual quality and may be similar in score to numerous rural mixed agrarian and woodland environments around the world, such as in Poland, Romaina, or Hubei Province, PR China. It may be reasonable to consider the extensive shorelines, vast expanse of national forest lands (3 national forests), state forest lands, national parks, 99 state parks, a national lakeshore, wildlife refuges, woodlots, and agrarian landscapes assist in maintaining a relatively preferred environment [35]. According to the map (**Figure 8**), the impact of the urban and industrial development is not widespread in the state and has not yet affected the state with large megalopolis expanses. Without attempting to be promotional, the state remains a big fishing (salmon, trout, walleye, northern, pan-fish), hunting (black bear, elk, white-tailed deer, and wild turkey), recreation state (camping, boating, hiking, cross country trails, snowshoeing trails, biking trails, snowmobiling trails, and horseback-riding—actually thousands of miles of trails) and in the Upper Peninsula there are 150 waterfalls. Michigan has also been rated as one of the top places in the world for watching sunrises on earth [36]. Such features are promoted in Michigan's 'Pure Michigan' tourist campaign [37]. The state has the most diverse agricultural economy after California [36]. Michigan produces cherries, apples, blueberries, many vegetable crops, nursery plants, surgarbeets, a strong wine producing industry, potatoes, dairy, cattle, hogs, chickens, turkeys, timber, hardwoods for flooring and furniture, and paper pulp, as well as the staple corn, soybeans, and winter wheat. While the state is associated with the famed mid-west rust-belt and the failures of Detroit, the overall impact upon the state's extensive forested lands and agrarian landscape is relatively minimal [38]. In addition, visitors to the Detroit metropolitan area often are surprised with the activity and prosperous nature of the Detroit metropolitan area. The Detroit metropolitan area is a distributed urban environment with no dominant central district, the opposite of many other large cities which are more concentrated in the core such as a city like Shanghai, P.R. of China. The state is a major constituent of the third coast, the longest coastline associated with the United States and Canada (excluding the Arctic) [39]. The results of this study reflect the impression that

any pairs of images to be notably different as perceived by respondents.

and viewed by respondents as at least somewhat scenic.

**4.2. Applications of the map**

**Figure 8.** A map of the predicted environmental/visual quality of the Michigan landscape. Green areas are environments with good visual quality. Blue, magenta, red, and orange indicate successively less preferred environments. Yellow areas are the least preferred.

number found in a standard table for such a Chi-square is 52.336 (a 99.5% confidence level (p ≤ 0.005) for 29 degrees of freedom. Since 54.267 is larger than 52.336, the predicted scores and the actual scores are in agreement at a 99.5% confidence level (p ≤ 0.005). These results suggest that it is possible to construct an environmental/visual quality map of Michigan that is relatively statistically reliable (**Figure 8**). In other words it is possible to predict the visual quality of any site in Michigan by knowing the land-use and to accurately predict the expected visual quality correctly 199 times in 200 attempts for any one attempt.

## **4. Discussion**

#### **4.1. Understanding the map**

To interpret the scores, Burley notes that scores in the 30s indicate highly preferred environments [23]. From randomly selected sites across the study area, no landscape scored in this category. The study revealed numerous landscapes with scores in the 40s and 50s. Such landscapes are often modestly preferred environments. Scores in the 70s are less preferred and scores near or above 100 are not preferred [23]. Across the state, least preferred environments were rarely encountered, primarily in the southeastern portion of the state. The 95% confidence interval for any scores is ±5 points [33]. Thus, it takes a separation of 10 points for any pairs of images to be notably different as perceived by respondents.

#### **4.2. Applications of the map**

number found in a standard table for such a Chi-square is 52.336 (a 99.5% confidence level (p ≤ 0.005) for 29 degrees of freedom. Since 54.267 is larger than 52.336, the predicted scores and the actual scores are in agreement at a 99.5% confidence level (p ≤ 0.005). These results suggest that it is possible to construct an environmental/visual quality map of Michigan that is relatively statistically reliable (**Figure 8**). In other words it is possible to predict the visual quality of any site in Michigan by knowing the land-use and to accurately predict the expected

**Figure 8.** A map of the predicted environmental/visual quality of the Michigan landscape. Green areas are environments with good visual quality. Blue, magenta, red, and orange indicate successively less preferred environments. Yellow areas

To interpret the scores, Burley notes that scores in the 30s indicate highly preferred environments [23]. From randomly selected sites across the study area, no landscape scored in

visual quality correctly 199 times in 200 attempts for any one attempt.

**4. Discussion**

are the least preferred.

122 Land Use - Assessing the Past, Envisioning the Future

**4.1. Understanding the map**

The average expected score (the sum of the products between the number of grid cells by their expected score, divided by the total number of grid cells) for the whole state is approximately 47.4. This score suggests that collectively the whole state of Michigan may not be the most beautiful of all environments; on the other hand, the environmental score is quite respectable and viewed by respondents as at least somewhat scenic.

To understand the context of the average expected score in Michigan, it is useful to explore the land-uses, published perceptions, recreational activities, and agriculture within the state. In an article published on the 29th of June, 2015, the Detroit Free Press reported that Thrillist who ranked all of the states in America, placed Michigan as the top state [34]. While the results of the list do not definitively demonstrate that Michigan is at the top, it does indicate that the environment of Michigan merits consideration as a noteworthy place in regard to visual quality and may be similar in score to numerous rural mixed agrarian and woodland environments around the world, such as in Poland, Romaina, or Hubei Province, PR China. It may be reasonable to consider the extensive shorelines, vast expanse of national forest lands (3 national forests), state forest lands, national parks, 99 state parks, a national lakeshore, wildlife refuges, woodlots, and agrarian landscapes assist in maintaining a relatively preferred environment [35]. According to the map (**Figure 8**), the impact of the urban and industrial development is not widespread in the state and has not yet affected the state with large megalopolis expanses. Without attempting to be promotional, the state remains a big fishing (salmon, trout, walleye, northern, pan-fish), hunting (black bear, elk, white-tailed deer, and wild turkey), recreation state (camping, boating, hiking, cross country trails, snowshoeing trails, biking trails, snowmobiling trails, and horseback-riding—actually thousands of miles of trails) and in the Upper Peninsula there are 150 waterfalls. Michigan has also been rated as one of the top places in the world for watching sunrises on earth [36]. Such features are promoted in Michigan's 'Pure Michigan' tourist campaign [37]. The state has the most diverse agricultural economy after California [36]. Michigan produces cherries, apples, blueberries, many vegetable crops, nursery plants, surgarbeets, a strong wine producing industry, potatoes, dairy, cattle, hogs, chickens, turkeys, timber, hardwoods for flooring and furniture, and paper pulp, as well as the staple corn, soybeans, and winter wheat. While the state is associated with the famed mid-west rust-belt and the failures of Detroit, the overall impact upon the state's extensive forested lands and agrarian landscape is relatively minimal [38]. In addition, visitors to the Detroit metropolitan area often are surprised with the activity and prosperous nature of the Detroit metropolitan area. The Detroit metropolitan area is a distributed urban environment with no dominant central district, the opposite of many other large cities which are more concentrated in the core such as a city like Shanghai, P.R. of China. The state is a major constituent of the third coast, the longest coastline associated with the United States and Canada (excluding the Arctic) [39]. The results of this study reflect the impression that the state is still predominantly a rural environment and is modestly beautiful in a 'low-key' manner. In other words, the average expected score and the existing landscape condition are mutually similar in expectations.

The map resulting for this study is an image representing the spatial perceptions of the respondents concerning the quality of the landscape across the state. As the land is managed and developed, the scores can be recomputed to estimate the perceived changes (both improvements and degradation) for the state. With additional research, the map could be considered a metric of the state's general perceived environmental quality. Across the globe, people are concerned about the impacts of human's transforming the environment; yet, comparatively, the compiled total environmental quality has not appreciatively changed since the arrival of Europeans, Africans, and Asians (a score predicted to be around 43 between the years 1816 and 1858 to a score of 47.4 in the year 2001, which is not significantly different in perceived quality) [40]. **Figures 9**–**11** are images from the landscape with scores near the current expected mean for much of the state. Notice none of the images are spectacular; however, none of the images age dismal either.

The results of this study and related studies indicate that land cover type is a strong predictor of visual/environmental quality. In planning and design circles, the nuances of the built environment are heavily debated with small details carefully examined by experts. Yet respondents evaluate these cover types relatively uniformly. In other words the refined changes and differences observed by experts and taught in planning and design schools are not necessarily observed/detected by the public. For experts the differences between communities can be quite distinct. As Charles Jencks indicated, there are at least two levels of cognition: the expert's and the public's [41]. For example, architects debate the merits of buildings; however, the research suggests that a poor building in a warehouse district or a noted piece of architecture such as Frank Lloyd Wright's Falling water house are perceived as simply structures and

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**Figure 11.** An image of a great lakes coastline, generating a score of 47.8.

the less of them comprising the view, the more the environment is preferred [23].

Similarly, landscape architects debate the merits of various plant material and landscape settings; yet, the public sees a noble red pine tree (*Pinus resinosa* Sol. Ex Aiton.) or a weedy boxelder tree (*Acer negundo* L.) as basically the same thing—a tree. It is only when the plant has ornamental flowers, is there an increased appreciation during the period of flowering. Within any cover type, there is variation (scores can range up or down), but the expected mean within the cover type is quite consistent when measured repeatedly across time, across locations, and among respondent groups in America and Europe. In other words, the most important characteristic that the planner or designer makes concerning visual/environmental quality may be in determining the cover type for a parcel of land, not the refined details of a design. In the future, designers may develop improved techniques to mask human intrusions and abundantly incorporate preferred features such as wildlife, flowers, and views of distant landscapes, creating strong variation in the range of scores possible for a given cover type. Then the covariation of land cover type with visual/environmental quality may no longer hold true. The relationships between the perception of environmental quality and cultural settings are explored in the book From Eye to Heart: Exterior Spaces Explored and Explained [42]. This book begins with discussing expectation values concerning the environment. Then the book examines some of the many interpretations for planning and designing across the globe and

**Figure 9.** An image of a modest rural residential setting collected in the study area with a score of 46.6.

**Figure 10.** An image of a typical agricultural landscape found in much of Michigan. This image produced a score of 44.1.

**Figure 11.** An image of a great lakes coastline, generating a score of 47.8.

the state is still predominantly a rural environment and is modestly beautiful in a 'low-key' manner. In other words, the average expected score and the existing landscape condition are

The map resulting for this study is an image representing the spatial perceptions of the respondents concerning the quality of the landscape across the state. As the land is managed and developed, the scores can be recomputed to estimate the perceived changes (both improvements and degradation) for the state. With additional research, the map could be considered a metric of the state's general perceived environmental quality. Across the globe, people are concerned about the impacts of human's transforming the environment; yet, comparatively, the compiled total environmental quality has not appreciatively changed since the arrival of Europeans, Africans, and Asians (a score predicted to be around 43 between the years 1816 and 1858 to a score of 47.4 in the year 2001, which is not significantly different in perceived quality) [40]. **Figures 9**–**11** are images from the landscape with scores near the current expected mean for much of the state. Notice none of the images are spectacular; however,

**Figure 9.** An image of a modest rural residential setting collected in the study area with a score of 46.6.

**Figure 10.** An image of a typical agricultural landscape found in much of Michigan. This image produced a score of 44.1.

mutually similar in expectations.

124 Land Use - Assessing the Past, Envisioning the Future

none of the images age dismal either.

The results of this study and related studies indicate that land cover type is a strong predictor of visual/environmental quality. In planning and design circles, the nuances of the built environment are heavily debated with small details carefully examined by experts. Yet respondents evaluate these cover types relatively uniformly. In other words the refined changes and differences observed by experts and taught in planning and design schools are not necessarily observed/detected by the public. For experts the differences between communities can be quite distinct. As Charles Jencks indicated, there are at least two levels of cognition: the expert's and the public's [41]. For example, architects debate the merits of buildings; however, the research suggests that a poor building in a warehouse district or a noted piece of architecture such as Frank Lloyd Wright's Falling water house are perceived as simply structures and the less of them comprising the view, the more the environment is preferred [23].

Similarly, landscape architects debate the merits of various plant material and landscape settings; yet, the public sees a noble red pine tree (*Pinus resinosa* Sol. Ex Aiton.) or a weedy boxelder tree (*Acer negundo* L.) as basically the same thing—a tree. It is only when the plant has ornamental flowers, is there an increased appreciation during the period of flowering. Within any cover type, there is variation (scores can range up or down), but the expected mean within the cover type is quite consistent when measured repeatedly across time, across locations, and among respondent groups in America and Europe. In other words, the most important characteristic that the planner or designer makes concerning visual/environmental quality may be in determining the cover type for a parcel of land, not the refined details of a design. In the future, designers may develop improved techniques to mask human intrusions and abundantly incorporate preferred features such as wildlife, flowers, and views of distant landscapes, creating strong variation in the range of scores possible for a given cover type. Then the covariation of land cover type with visual/environmental quality may no longer hold true.

The relationships between the perception of environmental quality and cultural settings are explored in the book From Eye to Heart: Exterior Spaces Explored and Explained [42]. This book begins with discussing expectation values concerning the environment. Then the book examines some of the many interpretations for planning and designing across the globe and through time. The book concludes with the relationship between environmental science and the built/managed landscape. This book provides some greater and broader context to the visual mapping study presented in this investigation.

**5. Conclusions**

of additional areas.

**Author details**

, Chung Qing Liu2

1 Namk Kemal University, Tekirdag, Turkey

3 Michigan State University, USA

\*Address all correspondence to<EMAIL_ADDRESS>
2 Jiangxi Agricultural University, Nanchang, PR China

Academy of Sciences. 2015;**112**(28):8567-8572

Rüya Yilmaz1

**References**

Predictive, respondent based models have been constructed to measure environmental and visual quality. This work is based upon over 50 years of research by investigators in the social, recreational, and planning and design disciplines/profession. The attributes of the landscape can be measured to form reliable maps of environmental/visual quality, providing a metric to assess landscapes, including urban landscapes. We were able to produce such a metric map for Michigan. We believe our approach allows investigators to evaluate these visions and assess, measure, and quantify environmental perceptions. Furthermore, we believe the methods are reproducible, allowing investigators around the world to produce similar maps

A Visual Quality Prediction Map for Michigan, USA: An Approach to Validate Spatial Content

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and Jon Bryan Burley3

[1] Martin B, Ortega E, Otero I, Arce RM. Landscape character assessment with GIS using map-based indicators and photographs in the relationship between landscape and

[2] Bratman GN, Hamilton JP, Hahn KS, Daily GC, Gross JJ. Nature experience reduces rumination and subgenual prefrontal cortex activation. Proceedings of the National

[3] Dupont L, Antrop M, Van Eetvelde V. Does landscape related expertise influence the visual perception of landscape photographs? Implications for participatory landscape

[4] Filova L, Vojar J, Svobodova K, Sklenicka P. The effect of landscape type and landscape elements on public visual preferences: Ways to use knowledge in the context of landscape planning. Environmental Planning and Management. 2015;**58**(11):2037-2055 [5] Meagher BR, Marsh KL. Testing an ecological account of spaciousness in real and virtual

planning and management. Landscape and Urban Planning. 2015;**141**:68-77

roads. Journal of Environmental Management. 2016;**180**:324-334

environments. Environment and Behavior. 2015;**47**(7):782-815

\*

#### **4.3. Limitations**

Additional research should be conducted to refute, validate, or refine the findings presented here. However, it may be surprising that with only 60 images from across the state, the selected images facilitate a significant statistical result and make a reliable map for a whole state. In some studies, weakly developed experiments with large data sets and numerous observations may produce unremarkable results. The results presented in this paper follow a philosophy that relies upon methodologies that have yielded significant results (such as employing Q-sort techniques as opposed to Likert scales), an exploration of reliable predictors, and non-parametric statistical procedures. We would like to believe that well-grounded, focused, simple experiments often yield results that some expensive and elaborate studies fail to yield. The senior investigator in this study had spent over 30 years, carefully plotting each step, conducting the next logical step/experiment before proceeding. So each study is simple (for example the statistics were done on a spreadsheet), yet yielding meaningful results. This philosophical approach has served the team well and we encourage others to have this level of insight and commitment when formulating environmental quality studies. Investigators sometimes believe that technology, huge sample sizes, and big money make impressive research.

The results produced in the map are dependent upon the quality of the land cover type map. Many cover maps concentrate upon the great variation of vegetation and naturalistic cover types. Urban and suburban cover types are rarely produced with the same level of sensitivity. Brady et al. at the University of Waterloo developed an excellent example concerning the classification of human disturbed areas, based upon morphological and ecological features [32]. Yet this level of description has not yet permeated many land cover maps. We believe that classification systems that adopt ideas embedded in work by investigators such as Brady et al. will produce higher quality maps in urban areas than the maps that are currently produced [32].

In the landscape there are several types of cover types that exist in the landscape such as large sand dunes, mud flats, and bare rock that may be beyond the predicative capabilities of this study. These more rare landscape types were neither studied in the prediction models nor in creating the map presented in this study. Cover types such as these would need to be studied in detail to make a more comprehensive and complete map. This is computed to be a substantial area of land, approximately 1700 km2 . Yet this area is only about 0.68% of the land of Michigan.

The resolution of the map is relatively coarse (2.207 km2 of land). Large maps with finer resolution will yield more refined results. A grid cell of that size will certainly have variation in it. The score represents the mean expected value within the cell.


**4. Concluding remarks**

The need for evaluation and validation during the development of bionic design projects en‐ abling to measure product success in meeting efficiency targets and proposed requirements, was one of the evident missing features of previously existing methodologies for bionic de‐ sign and which were met with the proposed methodology. Besides these aspects, the pro‐ posed method is also intended to support an iterative approach in conducting design projects in order to achieve optimal results and correct the detected deviations meeting the proposed objectives and needs. Implementation of the proposed method in practice aims its validation and also confirmation of the gains introduced in projects that follow the method‐ ology for the process of design with inspiration taken from Nature. This is explicit from the results obtained during the two projects, which in addition to validate the method, serve as a complement to present the method.

Bionic design, a discipline capable of enriching projects with gains in efficiency, aesthetics and sustainability and with a wide margin for improvement and with a whole world where inspiration can be reaped from, will certainly bring benefits to designers in the future devel‐ opment of their concepts and their research. One of the studies included in this theme that could be accomplished in the future, with the objective of its development and expansion, would be an empirical study made by surveying designers in businesses that would allow identifying the actors who make use of the bionic methodology, principles and approach in everyday professional life. With the same objective, the application of a methodology for comparative analysis of the gains brought by bionics to a wide range of products would be equally interesting. It is also important to note that the method presented in this work, like any other, is not considered perfect or timeless. The evolution of scientific and biological knowledge, emerging technologies and the principles of sustainability provide new insights and new creative processes and designs. The design method should therefore be seen as a process of constant improvement, optimization and evolution – as in Nature.


**3.2. Data collection**

A non-probability sample was used, based on quotas, proportional to the tourist destinations visited in Chile according to the National Tourism Service [81], divided into beaches (36%), lakes (25%) and other attractions (39%). The surveys were conducted in the main capital cities of Chile. Data were collected in the third and last stage. The survey was conducted on a total sample of 750 people, using the last tourist destination where they stayed as reference. A psychometric analysis of the data was conducted in order to obtain scales with a good level of reliability, validity and dimensionality. At the same time, a separate analysis of the multidimensionality of the hedonic benefit scale was conducted (see **Table 2**). The results of the partial analysis and the global model were both satisfactory.

#### **Affective evaluation (Affev)**

**Satisfaction (Sat)**

**Functional benefit (Fube)**

**Hedonic benefit visual attractions (Hbviat)**

**Hedonic benefit escape from routine (Hbero)**

**Hedonic benefit intrinsic pleasure (Hbinple)**

**Cognitive perception environment (Copen)**

**Hedonic benefit recreation (Hbere)**

**Symbolic benefit (Sben)**

Satisfaction Sat 1 This is the best place I have visited

42 Mobilities, Tourism and Travel Behavior - Contexts and Boundaries

Hedonic benefit visual attractions Hbviat 1 I love the appearance of this place

Hedonic benefit escape from routine Hbero 1 This place is an escape from routine

Hedonic benefit recreation Hbere 1 This is a great place to have fun

Symbolic benefit Sben 1 This place reflects who I am

Cognitive perception environment Copen 1 This place is known to be very safe

Functional benefit Fube 1 Vacationing in this place was just what I needed

Sat 2 This place is what I expected Sat 3 This place fulfills my expectations Sat 4 This place is exactly what I imagined Sat 5 This place was my best choice

Hbviat 2 I love the aesthetics of this place Hbviat 3 This place is a pleasure for my senses

Hbere 3 This is a place to enjoy life

Hedonic benefit intrinsic pleasure Hbinple 1 This place is fascinating to visit, compared to other possible activities

Fube 2 In this place, I found the vacation I was looking for Fube 3 Vacationing in this place is always convenient

Hbero 2 This place makes me feel I'm in a different world

Hbere 2 This place is very exciting, which is contagious

Hbinple 2 I wish I could be in this place all the time

Sben 2 This place is in tune with the way I see myself Sben 3 I identify with the people who choose this place

Copen 3 This place is known for its good transport system Copen 4 This place is known to have adequate signage

Sben 4 This place is visited by people like me

Copen 2 This place is known to be clean

Fube 4 Compared to similar places, this is the best vacation spot

**Table 1.** Measurement scales.

Then, an exploratory factor analysis and a confirmatory factor analysis (SEM) were conducted, along with different reliability analyses applying Cronbach's alpha, construct reliability and variance extracted (AVE). In order to identify items unattached to their dimension, principal component factor analyses were conducted with varimax rotation [82]. Following this procedure, there was no need to eliminate indicators from the scales analyzed (see **Table 3**). In fact, they all featured a good level of unidimensionality, with factor loadings well over 0.4 [83]. Considering the different scales, through structural equations, a confirmatory factor analysis was developed in order to confirm if the indicators or variables were adequate for an appropriate adjustment of the model. The requirements considered were the three criteria proposed by [84]. The first is to eliminate the indicators with a weak condition of convergence with their corresponding latent variable. A Student's *t* higher than 2.28 (*p* = 0.01) was used as a requirement. The second criterion is to separate from the analysis those variables with loadings translated into standardized coefficients lower than 0.5. Finally, indicators with a linear relationship *R*2 lower than 0.3 must be eliminated. For this process, the statistics pack AMOS SPSS version 23 was used. No indicators were eliminated according to any of the three criteria in this analysis. The adjustment indexes of this confirmatory factor model were acceptable: IFI 0.906, CFI 0.905, RMSEA 0.074, Normed χ2 5.08.

Once the optimal model was verified, the reliability of each scale was confirmed. Three tests were applied for this: Cronbach's alpha (limit 0.7), composite construct reliability (limit 0.7) [85] and analysis of variance extracted (limit 0.5) [86]. Results show that, in all cases, the minimum values defined (see **Table 4**) by these parameters of reliability are met.

**Table 2.** Multidimensional analysis of hedonic benefit.

**Table 3.** Exploratory factor analysis.

**Table 4.** Reliability of scales.

 Exploratory factor analysis.

Fube 2 0.90 Fube 3 0.86 Fube 4 0.80

Hbviat 2 0.91 Hbviat 3 0.85

Hbero 2 0.93

Hbere 2 0.88 Hbere 3 0.87

Hbinple 2 0.89

Sben 2 0.91 Sben 3 0.88 Sben 4 0.82

Copen 2 0.79 Copen 3 0.85 Copen 4 0.86

Affev 2 0.91 Affev 3 0.89

Sat 2 0.87 Sat 3 0.88 Sat 4 0.83 Sat 5 0.88 **explained %**

**Own value**

Validity was confirmed in terms of content and construct. The scales show an adequate level of validity in terms in content, due to the deep analysis of the literature, a critical incident study with tourists and then a clearing process of this scale through different focus groups with tourists and interviews with experts and sales executives from travel agencies. In order to meet the construct validity, it was determined if the scales proposed, already cleared, meet the convergent and discriminant validity. Convergent validity was confirmed by observing that all the standardized coefficients of the confirmatory factor analysis (AFC) were statistically significant to 0.01 and higher than 0.5 [87]. In order to verify the existence of discriminant validity, a confidence interval test was used [88]. It consists in building the confidence intervals resulting from the correlations among the different latent variables that make up the confirmatory factor model (AFC) (see **Table 5**). According to this test, the model has discriminant validity since no confidence interval contained the value 1 [89]. The methodological process developed makes it possible to conclude that the proposed model shows a good level of overall validity.

**Table 5.** Discriminant validity.

Validity was confirmed in terms of content and construct. The scales show an adequate level of validity in terms in content, due to the deep analysis of the literature, a critical incident study with tourists and then a clearing process of this scale through different focus groups with tourists and interviews with experts and sales executives from travel agencies. In order to meet the construct validity, it was determined if the scales proposed, already cleared, meet the convergent and discriminant validity. Convergent validity was confirmed by observing that all the standardized coefficients of the confirmatory factor analysis (AFC) were statistically significant to 0.01 and higher than 0.5 [87]. In order to verify the existence of discriminant validity, a confidence interval test was used [88]. It consists in building the confidence intervals resulting from the correlations among the different latent variables that make up the confirmatory factor model (AFC) (see **Table 5**). According to this test, the model has discriminant validity since no confidence interval contained the value 1 [89]. The methodological process developed makes it possible to conclude that the proposed model shows a good level

 **(df)**

**Bi-variate relationship Confidence intervals Difference** *χ***2**

46 Mobilities, Tourism and Travel Behavior - Contexts and Boundaries

Symbolic benefit—satisfaction 0.61–0.64 1956.2(1)

Satisfaction—affective evaluation 0.53–0.55 1972.6 (1) Hedonic benefit—satisfaction 0.82–0.85 1956.4 (1) Satisfaction—functional benefit 0.81–0.85 1966.3 (1) Satisfaction—cognitive perception 0.50–0.54 1955.9 (1) Symbolic benefit—hedonic benefit 0.72–0.75 1959.8 (1)

Symbolic benefit—functional benefit 0.60–0.63 1958.4 (1)

Hedonic benefit—functional benefit 0.84–0.86 1957.7 (1) 1955.9 (385)

0.47–0.50 1961.6 (1)

0.41–0.42 1988.7 (1)

0.47–0.48 1992.7 (1)

0.51–0.53 1971.0 (1)

0.66–0.67 1996.4 (1)

0.61–0.62 1979.3 (1)

0.42–0.45 1962.0 (1)

of overall validity.

**Confidence interval test**

Cognitive perception—functional

Cognitive perception—affective

Symbolic benefit—affective

Hedonic benefit—cognitive

Hedonic benefit—affective

Affective evaluation—functional

Symbolic benefit—cognitive

**Full model**

benefit

evaluation

evaluation

perception

evaluation

benefit

perception


5.1.1 2-D Euler simulations using AVUS

The four-point star configuration, Figure 14, was selected as the test case. The scramjet forebody-inlet-isolator model was exposed to a Mach 5 hypersonic freestream flow-field at a zero angle of attack. Figures 17–19 presents the 2-D Euler simulation results along the centerline of the four point star configuration. On examining these figures, the following observations are made. Figure 17 presents velocity distribution data for the geometry, where it is observed that the behavior of the flow imitates the conceptual flow-field presented in Figure 4. That is, freestream flow is first processed by the 2-D oblique shock, travels parallel to the wedge surface, is processed again by the reflected shock and travels parallel to the isolator duct walls. Figures 18 and 19 presents the density and pressure flow-field

Figure 29. Pressure contours at the isolator exit.

distributions. Once more we observe the organized nature of the 2-D flow, which is supported by the constant property in the respective zones. The development of the shock train within the isolator duct is also captured in these figures.


Ankylosing Spondylitis - Recent Concepts

*Edited by Jácome Bruges Armas*

Published in London, United Kingdom

Ankylosing Spondylitis - Recent Concepts http://dx.doi.org/10.5772/intechopen.98070 Edited by Jácome Bruges Armas

#### Contributors

Razvan Adrian Ionescu, Horatiu Popoviciu, Florentin Ananu Vreju, Esra Dilsat Bayrak, Malini Alexander, Rajeshwar Nath Srivastava, Shatakshi Pant, Sudeepti Ratan Srivastava, Amar Chandra Sharma, Saloni Raj, Jácome Bruges Armas

#### © The Editor(s) and the Author(s) 2023

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First published in London, United Kingdom, 2023 by IntechOpen IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, 5 Princes Gate Court, London, SW7 2QJ, United Kingdom

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4.1 Lifestyle

4. Discussion

Cardiorespiratory Fitness

Figure 2.

(22.42 kg/m2

70

Within this study, we aimed to explore the relationship between physical activ-

). A large scale sample (n = 1023) of female students of the University

ity and body composition among young females. The results showed expected correlation. Specifically, most results (SMM%, BFM%, VFA, FMI, FFMI) showed statistically relevant correlation (p > 0.001) with lifestyle variables, with lifestyle score being a somewhat stronger predictor in comparison to other variables. This cross sectional study determined the existence of correlation between physical activity and certain parameters of body composition, as well as of the constructed mathematical model of body composition score for young women. Our correspondents had normal (some say optimal) weight according to BMI

of Belgrade aged 21.3 years on average, average BMI was 20.7 kg/m2 [50]. This difference could be explained by the fact that our correspondence are not only older by 3 years on average, but also by the fact that our sample includes working women, women who gave birth in addition to students, which can influence their health

According to cutoffs % fat Heyward [51], young women from our research belong to the category of above average fat occurrence, which is unfavorable considering the likely increase in body fat during aging, especially related to the menopause [52]. Gába and Přidalová [53] applied the same method of measuring body composition among Czech women of the same age group (18–30 years). The correspondents had lower values of all variables that have fat component, that is, BM, BMI, %BFM, and VFA. There is a clear correlation between excess body fat, which is a basic characteristic of obesity and increased mortality among different ethnic groups around the world. Yet, it is clinically relevant that obesity is heterogeneous, and there are individual differences in regional fat distribution, especially in

habits related to physical activity, nutrition, etc.

Linear regression of body composition and lifestyle scores.

Out of the maximum score of 15 for physical activity, our correspondents had 9.29 3.72, which can be considered to be a moderate activity. Close to 50% of them said to be active over 4 h per week, which could be said to correspond to the recommendation of 30 min of exercising each day [58]. Still, only a third has been continuously active throughout the year, which is near to the percentage of those who described their lifestyle as very active. Half of the correspondents spend their free time in sedentary activities (using the phone, computer, watching TV, reading and the like).

With regard to the mathematical model, the results of the combined influences of individual variables of body composition showed that FFMI and SMM% (4.894 vs. 1.353) had the greatest influence on the point score of body composition, while BW = 0.092 had the lowest influence (please see the formula). Apparently, taken overall variability of the point score, the most influential to the optimal model of body composition were those variables that define fat-free muscle mass dependent on longitudinality (FFMI), or the percentage of muscle tissue in the organism independent of voluminosity (SMM%). As both cases apply to contractile components, that is, body components most responsible for motorical quality, that is, movement, the results clearly showed that fat-free body structure was the most sensitive body component for defining the optimal body composition within the defined optimal body model among young females from the investigated sample. In addition, the two abovementioned most sensitive variables of the model are body components that are directly developed by physical activity, regardless of that activity being endurance training or resistance or weight training. Thereby, it has been directly shown that young females were no different in relation to the occurrence of body fat in the body—as a variable directly linked to diet and sedentary lifestyle, but dominantly differed by the occurrence of contractile component—as a variable directly dependent of the amount of physical activity or exercising.


Acknowledgements

Costa W.A. (1427204/2014), Cunha V.M.B. (1566277/2015), Oliveira M.S. (1662230/2016), Silva M.P. (1636612/2016), Bezerra F.W.F. (1718822/2017), Pinto R.H.H. (1662902/2016) thank CAPES for the doctorate scholarship.

[6] Herrero M, Mendiola JA, Cifuentes A, Ibáñez E. Supercritical fluid extraction: Recent advances and applications. Journal of Chromatography. A. 2010;1217:2495-2511. DOI:

Carbon Dioxide Use in High-Pressure Extraction Processes

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Contents

### **Preface XI**

Preface

features and advantages.

establishing a new position.

establishing a paradigm.

investigation.

sensibility ergonomics approach is introduced.

In general, woven fabrics are known as the traditional textile fabrics for apparel manufacturing and are used widely in various fabric compositions as intermediate goods that affect human activities. The relative importance of woven fabrics as traditional textile materials is extremely large and currently application fields of woven fabrics as technical textiles are rapidly expanded by utilizing its geometric

In the modern world the concept of "woven fabrics" is changing from the traditional material for garment fabrics to a key material in advanced industry in terms of IT, NT, BT, ET, ST and is being rapidly applied in the field of textile composite materials,

To do this the existing definitions rather than technology for the textile industry can contribute to a convergence switch fabric and a new concept for the idea of

In this text, the important key role of woven fabrics is introduced through the author's differentiated and specific research results. Using this text, micro-fibre fabric manufacturing and its importance as a structural material has been introduced, and dimensional and geometrical analysis of woven fabrics are described quantitatively using their algorithms. Processing technology and hydrolysis resistance of clothing textile materials is stated and the importance of fabrics as filtration media and the

Above all, what has been described herein is limited to the convergence feature and typical application examples of woven fabrics, but the important trend of woven fabrics as upgraded embedded or hybrid material has been presented through this

Finally, we sincerely thank the authors of this text for writing the manuscript, the Publishing Process Manager Ms. Ana Skalamera and to whom it may concern in InTech.

**Han-Yong Jeon**

Korea

GSRL (Geosynthetics Research Laboratory)

TOMRL (Technical Organic Materials Research Laboratory) Division of Nano-Systems Engineering, Inha University, Incheon,


**5. Conclusions**

These results suggest that measurement of urinary proteome was more promising than albuminuria alone for predicting renal outcomes in patients with type 2 diabetes. A panel of five proteins had the potential for use as a biomarker in clinical practice.

**Supplementary Materials:** Supplementary materials can be found at http://www.mdpi.com/1422-0067/21/12/ 4236/s1. Figure S1. Prognostic probability of 54 patients by RF and SVM classifier divided into three groups by albumin-to-creatinine ratio.; Table S1. Demographic and clinical characteristics of the 54 patients with type 2 diabetes.; Table S2. Search result of MS/MS spectra of protein sequences obtained from the 54 urine samples in the Human Swissprot proteome database using the SequestHT search engine.; Table S3. Results of volcano plot analysis.; Table S4. Normalized abundance of 412 urinary proteins in the 54 patients with type 2 diabetes.; Table S5. Univariate receiver operating curve analysis of the areas under the curves of 412 urinary proteins.; Table S6. Prognostic probability by RF and SVM classifier of 54 patients.

**Author Contributions:** Conceptualization, J.H.K., S.H.S., S.S.K., I.J.K., and K.K.; methodology, H.J.; formal analysis, H.-S.A.; investigation, J.Y. (Jiyoung Yu); data curation, H.-S.A., S.S.K., and I.J.K.; writing—original draft preparation, H.-S.A. and K.K.; writing—review and editing, J.H.K., J.Y. (Jiyoung Yu), J.Y. (Jeonghun Yeom), S.H.S., S.S.K., and I.J.K; visualization, H.-S.A.; supervision, I.J.K and K.K.; funding acquisition, J.H.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by a Biomedical Research Institute Grant (2017-20) from Pusan National University Hospital.

**Acknowledgments:** The authors thank the Department of Biostatistics, Clinical Trial Center, Biomedical Research Institute, Pusan National University Hospital.

**Conflicts of Interest:** The authors declare no conflict of interest.


**2.2. Fabrication technology examples**

to fabrication technology or according to the type of substrate used (e.g., c-Si, amorphous, polymeric) or according to application. Within each technology, the reference list has been sorted out chronologically to help interested readers follow the origin and evolution of ideas and technologies. Despite of the relatively large number of references included, this is not a comprehensive review. The reference list simply offers starting points. Getting back to the main theme, the question still remains: why does microfluidics continue to receive increased attention? What are

The science and technology mentioned above are widely exploited and applied to give microfluidics a host of advantages. A brief list includes use of small volumes of sample and reagents (thus reducing cost per analysis and minimizing waste disposal); rapid sample processing; potential for automation (thus reducing cost); reduced risk of contamination; short analysis time (e.g., by increasing speed of separations); small footprint and light-weight thus enabling development of future portable microfluidic-based, portable micro-instruments that can be employed *on-site* or for personal use or for personal dosimetry; potential for massive parallelism (for high sample throughput); and overall, lower ownership and operating costs (vis-à-vis conventional, lab-sized systems). Application areas (to name but a few), include analytical chemistry, synthetic chemistry (including nanomaterials synthesis), microbiology, biotechnology, point-of-care diagnostics, drug delivery, immunoassays and medicine, health-monitoring and health-diagnostics, agriculture, food safety and environmental monitoring [30–47].

Microchannel fabrication technology has been borrowed from the semiconductor industry. Initially, *bulk micromachining* [1–17, 48–51] was employed on crystalline Si (c-Si) substrates and on amorphous glass. To use it, a photolithographically patterned wafer was dipped into a chemical etching solution to etch-away (or subtract) material from the substrate, thus forming microchannels of desired geometry. This method is often referred to as *wet chemical etching* [48–51]. Inadequate control of channel depth (resulting unevenly etched channels) due to spatial etch-rate variations and to pyramid formation when crystalline-Si (c-Si) substrates and deep microchannels were etched are two key disadvantages. In contrast, *surface micromachining* [52–54] involves repetitive patterning, thin layer deposition and selective etching of sacrificial layers. The challenge here stems from the many photolithography steps involved and from

We used (as far back as the 1990's) cleanroom-based photolithography, bulk micromachining and **wet chemical etching** [48–51] to fabricate shallow-depth microchannels (with relatively low width-to-depth **aspect ratio**). This approach is often referred to as 2D sculpting of Manhattan-like structures and it offers a planar, 2D- rather than a 3D-perspective. Some

the advantages of using microfluidics, especially for chemical analysis applications?

**1.3. Advantages and selected applications of microfluidics**

4 Microfluidics and Nanofluidics

**2. Technology for fabrication of microfluidic channels**

**2.1. Fabrication using either crystalline Si (c-Si) or other substrates**

the precautions required so that previously deposited layers are not damaged.

examples will be briefly discussed later.

To highlight substrate-dependence of fabrication, the fabrication steps required for microchannels on c-Si and on amorphous glass or quartz substrates are compared and contrasted in **Figure 2**. It should be noted that depending on crystallographic orientation of the substrate and of the chemical cocktail used in the etching solution, isotropic or anisotropic etching may be obtained [48–51].

**Example 1:** *Planar 2D-chips* **and wet chemical etching for fabrication of microchannels on crystalline and amorphous substrates** (**Figure 2**).

**Figure 2.** Simplified steps used for fabrication of microchannels on a) a c-Si wafer as a substrate and on b), a wafer made from an amorphous material (abbreviated as a-wafer-above, such as glass).

For completeness, an example of wet chemically etched microchannels on glass is shown in **Figure 3**.

The quality of the etched microchannels depended on the composition of the etching solution and on the geometric-primitives that were used to define the channels. To enclose the microchannel of **Figure 3**, a cover plate was used (but is not shown in **Figure 3**). Depending on the required optical transparency, a UV-transparent quartz cover plate was employed for most of the work described here. Furthermore, depending on the substrate (e.g., c-Si or glass), a variety of bonding methods can be employed [2–17].

and time-delays involved. There is another limitation if microchannels are to be used with biological samples, because many biosamples adhere to substrates. Thus, functionalized surfaces

**Figure 3.** (a) Part of a 14.5 mm by 25.6 mm chip of an etched microfluidic channel on corning 7059 glass with the photoresist removed and (for clarity) without a cover plate. Also omitted are pipette-tips used as sample reservoirs that are attached to the sample well. A coin was included for size. (b) Part of a Mylar mask used for photo-lithography. (c) Part of a washed meandering microchannel shown under 10x magnification and (d) shown under 60-fold magnification. (e) an unwashed microchannel immediately after etching showing etching by-products inside the microchannels, thus requiring their removal. (f) a sample-well and a washed microchannel showing the quality of etching, in particular for the round sample-well. For (d), (e) and (f) the photoresist was not removed to provide contrast for the photographs.

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**Example 2. Imprinting microchannels on planar polymeric 2D-chips.** 2D-microchannel fabrication on polymeric substrates is one way of overcoming some of the limitations mentioned above. But polymers may contain additives, fillers or plasticizers that may contaminate the samples, and they may display auto-fluorescence. As for fabrication (**Figure 4**), it may be achieved by using Si-stamp imprinting (**Figure 4**) or by imprinting (by pressing) a wire on a substrate [69] (**Figure 5**). In the example shown in **Figure 4**, a c-Si stamp (or master or hard

or microfluidic channels on polymeric substrates are preferred.

mold) was developed and was employed for replication by imprinting.

Despite of the ability to fabricate low aspect ratio microchannels, wet chemical etching has shortcomings arising from costs, from limited access by many to photolithography and to cleanrooms, and from time-delays between mask-design (**Figure 3b**) and receipt of finished prototype (e.g., **Figure 3a**). At present, access to cleanrooms is not required because microfluidic chips can now be ordered from specialized foundries. In spite of this, there are still costs Microfluidics and Nanofluidics: Science, Fabrication Technology (From Cleanrooms to 3D... http://dx.doi.org/10.5772/intechopen.74426 7

**Figure 3.** (a) Part of a 14.5 mm by 25.6 mm chip of an etched microfluidic channel on corning 7059 glass with the photoresist removed and (for clarity) without a cover plate. Also omitted are pipette-tips used as sample reservoirs that are attached to the sample well. A coin was included for size. (b) Part of a Mylar mask used for photo-lithography. (c) Part of a washed meandering microchannel shown under 10x magnification and (d) shown under 60-fold magnification. (e) an unwashed microchannel immediately after etching showing etching by-products inside the microchannels, thus requiring their removal. (f) a sample-well and a washed microchannel showing the quality of etching, in particular for the round sample-well. For (d), (e) and (f) the photoresist was not removed to provide contrast for the photographs.

For completeness, an example of wet chemically etched microchannels on glass is shown in

**Figure 2.** Simplified steps used for fabrication of microchannels on a) a c-Si wafer as a substrate and on b), a wafer made

The quality of the etched microchannels depended on the composition of the etching solution and on the geometric-primitives that were used to define the channels. To enclose the microchannel of **Figure 3**, a cover plate was used (but is not shown in **Figure 3**). Depending on the required optical transparency, a UV-transparent quartz cover plate was employed for most of the work described here. Furthermore, depending on the substrate (e.g., c-Si or glass),

Despite of the ability to fabricate low aspect ratio microchannels, wet chemical etching has shortcomings arising from costs, from limited access by many to photolithography and to cleanrooms, and from time-delays between mask-design (**Figure 3b**) and receipt of finished prototype (e.g., **Figure 3a**). At present, access to cleanrooms is not required because microfluidic chips can now be ordered from specialized foundries. In spite of this, there are still costs

a variety of bonding methods can be employed [2–17].

from an amorphous material (abbreviated as a-wafer-above, such as glass).

**Figure 3**.

6 Microfluidics and Nanofluidics

and time-delays involved. There is another limitation if microchannels are to be used with biological samples, because many biosamples adhere to substrates. Thus, functionalized surfaces or microfluidic channels on polymeric substrates are preferred.

**Example 2. Imprinting microchannels on planar polymeric 2D-chips.** 2D-microchannel fabrication on polymeric substrates is one way of overcoming some of the limitations mentioned above. But polymers may contain additives, fillers or plasticizers that may contaminate the samples, and they may display auto-fluorescence. As for fabrication (**Figure 4**), it may be achieved by using Si-stamp imprinting (**Figure 4**) or by imprinting (by pressing) a wire on a substrate [69] (**Figure 5**). In the example shown in **Figure 4**, a c-Si stamp (or master or hard mold) was developed and was employed for replication by imprinting.

**Example 3: 3D-printed, milli-sized fluidic channels on polymeric materials for hybrid 3D chips.** 3D printing technology [109–122] using polymeric materials is receiving attention for rapid prototyping [109] including fabrication of mm channels (often called **millifluidics**) and more recently for sub-mm channels (using specialized printers) [120, 121]. We used 3D-printing due to reduced fabrication and ownership costs and due to quick turn-around times (often from concept to prototype in hours). A simple, hybrid, 3D-printed 3D-chip containing a millifluidic channel is shown in **Figure 6**. The word *hybrid* was used because the two

**Figure 6.** Sugar cube-sized, 3D-printed hybrid-chip with a millifluidic channel to be fitted with a quartz cover plate (selected for UV transparency). A sample introduction system is also shown and it has been included to provide an overall size for this "critical" component of a potential future micro-instrument. An actual sugar-cube (~1 cm by ~1 cm)

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In my laboratory, some of the fabrication technologies discussed thus far have been used to constrain plasmas in microfluidic or in millifluidic channels. But why plasmas and why

There are four states of matter: gases, liquids, solids and plasmas [123–131]. To generalize, atmospheric pressure plasmas are ionized gases that are either hot or cold (about room temperature or somewhat above it). Plasmas occur in nature, for example those found in inter-stellar space, in the ionosphere, in auroras and in lightening. There are also artificially-generated plasmas that are being used in many every-day-life applications. Neon signs and fluorescent lights in which low-pressure plasmas are formed either in Neon (Ne) gas or in Argon (Ar) gas) are two such examples. Other examples include plasmas employed for device fabrication by the semiconductor industry or for materials synthesis in nanoscience and nanotechnology [129–131]. **It has been estimated that over 50%** of whatever goes inside any electronic device

**Conventional-scale** (or **lab-scale**) atmospheric pressure plasmas are widely used in **chemical analysis**, primarily in the form of atmospheric-pressure, **6000–10,000 K hot** Inductively Coupled Plasmas or ICPs [132]. Due to their size and weight (e.g., in the few 100's of pounds),

(e.g., a tablet, a smartphone, TV) is fabricated using a low-pressure plasma.

needle electrodes and the quartz cover plate were not 3D-printed.

microplasmas?

**3. Why plasmas?**

has been included for scale comparisons.

**Figure 4.** (a) Mask; (b) mask on c-Si chip, coin has been added for size; (c) chemically etched c-Si chip (serving as a stamp), the meandering pattern is protruding from the surface of the chip; (d) imprint generated by pressing the stamp and the polymeric chip together by placing them in a hydraulic press and by applying pressure at room temperature; (e) imprinted sample-well on a polymer chip shown under magnification; and (f), part of an imprinted meandering channel shown under magnification. For (d) and (e) and (f) different polymeric materials were used.

**Figure 5.** (a) Imprinted channel on a polymeric chip (60x magnification), (b) sample-well (60x magnification) and (c) Venturi micropump with no moving parts and no electrical power requirements fabricated by imprinting (coin included for size) [73].

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**Figure 6.** Sugar cube-sized, 3D-printed hybrid-chip with a millifluidic channel to be fitted with a quartz cover plate (selected for UV transparency). A sample introduction system is also shown and it has been included to provide an overall size for this "critical" component of a potential future micro-instrument. An actual sugar-cube (~1 cm by ~1 cm) has been included for scale comparisons.

**Example 3: 3D-printed, milli-sized fluidic channels on polymeric materials for hybrid 3D chips.** 3D printing technology [109–122] using polymeric materials is receiving attention for rapid prototyping [109] including fabrication of mm channels (often called **millifluidics**) and more recently for sub-mm channels (using specialized printers) [120, 121]. We used 3D-printing due to reduced fabrication and ownership costs and due to quick turn-around times (often from concept to prototype in hours). A simple, hybrid, 3D-printed 3D-chip containing a millifluidic channel is shown in **Figure 6**. The word *hybrid* was used because the two needle electrodes and the quartz cover plate were not 3D-printed.

In my laboratory, some of the fabrication technologies discussed thus far have been used to constrain plasmas in microfluidic or in millifluidic channels. But why plasmas and why microplasmas?